B-Cell Lymphoma ICYMI

A collaboration between clinicians and industrial engineers resulted in significant improvements in cancer screening, the development of toolkits, and more efficient care for hepatocellular carcinoma and breast, colorectal, lung, head and neck, and prostate cancers.

Cancer is one of the most common causes of premature death and disability that requires long-term follow-up surveillance and oftentimes ongoing treatment for survivors that can lead to important health, psychosocial, and economic consequences.^1-3 As life expectancy continues to rise, so does the incidence and prevalence of cancer and the number of cancer survivors.^4,5 At this time, cancer care in general is poorly coordinated, fragmented, and very complex.^6,7 Research indicates effective and high-quality cancer care in a timely fashion requires health care providers to function as a multidisciplinary team.^8-11 Thus, there is an ever-increasing need to improve the efficiency and efficacy of interventions throughout the entire cancer care continuum.

Like other cancer treatment systems, the VA faces some challenges in timeliness, surveillance, and quality of the cancer care process.^12-18 Although implementation of cancer patientcentered home care and other efforts were developed to improve delivery and efficiency of cancer care in VA and non-VA facilities, the patient continuum of care remains convoluted.^2,19-23

In 2004, the Clinical Cancer Care Collaborative (C4), a national VA program, was launched to improve timeliness, quality, access improvement, efficiency, and the “sustainability and spread” of successful programs at the VA. This program included representatives throughout the VA and encompassed cancer care coordinators (clinical nurse navigators), advisory panels, and a multidisciplinary team of clinicians.

In 2009, the VA promoted the Cancer Care Collaborative (CCC) to focus on optimizing the timeliness and quality of colorectal, breast, lung, prostate, and hematologic cancer care throughout the VA health care system. The VA Office of Systems Redesign (SR) partnered with the VA-Center for Applied Systems Engineering (VA-CASE) Veteran Engineering Resource Center (VERC), including industrial engineers (IEs) to provide their expertise and support. The CCC provided a forum to develop teams; set aims; and map, measure, analyze, and implement changes to assure timely diagnosis and initiation of evidence-based treatment and subsequently sustain the practices that led to improvements in these areas.

The CCC structure was separated into 6 distinct support areas: (1) industrial/systems engineering support; (2) informatics and clinical application support; (3) development and dissemination of improvement resource guides; (4) real-time and rapid-cycle evaluation tools and approaches; (5) application of advanced operational systems engineering techniques, such as simulation and modeling to inform further system optimization; and (6) advisory panels focused on quality topics that were identified, developed, implemented, and evaluated by the participants with support from the CCC faculty.

Here the authors describe the framework of the CCC model developed by VA-CASE, demonstrate the performance improvement results of teams focusing on several types of cancer, and highlight the key indicators to best practices.

Methods

Figure 1 outlines the CCC 3-Phase Conceptual Model. Phase 1 included diagnosis (screening and symptoms); phase 2 included treatment (from diagnosis to beyond treatment); and phase 3 was designed for hub and spoke facilities where screening/diagnosis occurs in a smaller (spoke) facility and treatment occurs in the larger (hub) facility.

In the first phase, 18 facility-based teams were selected through an application and interview process and immediately applied SR to their team’s specific improvement projects, which included the following cancers: breast, colorectal, lung, and prostate.

In addition to the cancer types covered in the initial phase, phase 2 also included hepatocellular carcinoma (HCC) and head and neck cancers. National VHA Toolkits were products that developed from and for use in lung and colorectal cancers (CRCs) (phases 1 and 2). These were organized and disseminated throughout the entire VA, offering specific knowledge and tools that could be applied to improving cancer care. The toolkit included guidance documents, specific process examples, and items that could be downloaded into Microsoft SharePoint (Redmond, WA) for adaptation and use by VA facilities. The toolkit contents were primarily developed and/or identified by CCC participants and funded by the VA Office of Quality and Performance (OQP) and SR. The toolkits included links to the following resources for each cancer type in phase 2: quality indicators, tool tables, timeliness measures, understanding the continuum of care, and a resource entitled, “How Can the Quality Metrics Help Me?” (eAppendix 1, available at fedprac.com/AVAHO).

Document

fp-34-5s-s50_eappendix_1.pdf

Media Folder

Media Root

 

The phase 3 collaborative was designed for hub and spoke facilities by focusing on current state vs ideal state processes, communication patterns, and care coordination between the hub and spoke facilities. There were 10 facilities in which all teams focused on lung cancer. Each facility was made up of 1 hub and had the ability to send up to 8 participants (from either the hub or the spoke facility) to the CCC workgroup meetings. Participants were specialists, radiologists, primary care providers, pathologists, nurses, nurse practitioners, or physician assistants.

Conceptual Model Deployment

The deployment of the CCC 3-phase conceptual model was based on the Institute for Healthcare Improvement (IHI) Breakthrough Series Collaborative Model.^24,25 Implementation was carried out over 3 phases (2005-2011) after proper teaching, coaching, and learning sessions (LS)

Each LS incorporated instruction in basic systems engineering and Lean Six Sigma principles (an approach to quality improvement that focuses on reducing waste and variability) with practical, health care-based examples, case studies, and immediate application of the VA-TAMMCS (vision/analyze, team/aim, map, measure, change, sustain) SR organizational framework (Figure 2), tools, and methodologies to the process under investigation.²⁶ The VA-TAMMCS (eAppendix 2, available at fedprac.com/AVAHO) 

Document

fp-34-5s-s42_eappendix_2.pdf

Media Folder

Media Root

was developed by the VA Office of SR to improve the care provided to veterans at VA facilities.²⁷ Between LSs, teams worked to test and refine existing and innovative improvements in their systems, and the teams shared the results of their improvement efforts in monthly reports in action periods.

The CCC encouraged joint facilitation. A SR clinical coach, a VERC IE, and participating facilities were required to work together intensively (mentor and support) for 10 to 12 months. The mix of clinicians and engineers helped the facilities by bringing in diverse perspectives, which led to better decisions in the improvement of cancer care.²⁸ During the CCC, the IEs partnered with and supported clinicians, using Lean Six Sigma and SR tools and approaches to health care quality improvement to quickly make improvements in efficiency and quality (eAppendix 3a, 3b, and 3c, available at fedprac.com/AVAHO).^26,29

  

The IEs provided on-site support at all participating VAMCs during all 3 phases by providing the clinical teams with a variety of VA-TAMMCS process improvement tools to support the analysis and improvement of their organizations.

Data Collection

As part of the overall improvement process, the facilities worked on several aim statements in order to improve a primary constraint; such as timeliness and quality of care. An aim statement communicates what you want to do (eg, reduce, improve, or eliminate), by how much, and when. In order to improve timeliness, the CCC focused on measures from first evidence to tissue diagnosis, from diagnosis to treatment, and also intermediate measures, such as time from positron emission tomography scan ordered to completion. While working on overall quality of care unique to cancer, the CCC focused on measures related to documentation compliance and consistency of care provided to patients.

Phase 1

Facilities were to optimize their process (time from initial suspicion to diagnosis). Hence, participating facilities were allowed to simply identify their aim statement and pick and choose the area of focus.

Phases 2 and 3

Timeliness Aims. These aims were addressed through improvements in information technology in the Computerized Patient Record System (CPRS) electronic medical record by creating electronic order sets containing codes that alert providers daily to retrieve and follow up on abnormal test results. Primary care physicians and front desk staff also were educated on the use of these order sets and to schedule a follow-up test or specialist consult within 3 to 7 days.

Aim 1: Reduce to 15 days the time from initial suspicion to diagnosis within 1 year.
Aim 2: Reduce to 30 days the time from diagnosis to start of treatment within 1 year.

Quality Aim. Improve the compliance rate of identified quality indicators to 100% within 1 year.

Measurement Tools

The Cancer Care Measurement Tools were designed to support the OQP performance metric (Figure 3). The OQP creates and collects data on evidence-based national benchmarks to measure the quality of preventive and therapeutic health care services at the facility, VISN, and national levels. These metrics may be performance measures, performance monitors, quality indicators, and special studies, among other measures, to support clinicians, managers, and employees in improving care to veterans.

 

For each of the 3 CCC phases, VA-CASE IEs facilitated the development of standardized measurement and tracking tools for each cancer type. The tools identified key timeliness and quality measures as a function of entered patient data (eAppendixes 4-7, available at fedprac.com/AVAHO). 

Document

fp-34-5s-s42_eappendix_4.pdf

Media Folder

Media Root

Document

fp-34-5s-s42_eappendix_5.pdf

Media Folder

Media Root

 

Document

fp-34-5s-s42_eappendix_6.pdf

Media Folder

Media Root

 

Document

fp-34-5s-s42_eappendix_7.pdf

Media Folder

Media Root

Each type of cancer tool contains data entry, measurement, and chart sheets. The users entered information in the data entry sheet, and measurements and charts were automatically generated. Charts were used during the CCC LSs to identify process constraints and bottlenecks as well as quality of care issues.

Quality Improvement Toolkit Series

The Quality Improvement Toolkit Series (QITS) was created for VA clinical managers and policy makers to improve diagnosis, treatment, and patient outcomes for high-priority conditions. The goal of the QITS is to serve as the cancer care improvement resource guide to produce and disseminate the National Quality Improvement Toolkit resource.

Each tool included in the QITS is matched to 1 or more metrics of the OQP (such as a performance measure or quality indicator). For example, the types of tools include CPRS order sets and templates, enhanced registries and patient databases, service agreements, and care process flow maps. Each toolkit served as a resource for improving facility performance on a specific set of established performance measures and/or quality indicators. Toolkits that helped VA facilities improve performance on OQP quality indicators and performance measures were based on the VA-TAMMCS model and continuous improvement that was tailored to the structure and needs of the VA system. The VA-CASE staff provided guidance on the criteria for inclusion in the toolkits to promote best practice and quality in clinical practice. The criteria used by a condition-specific expert panel were based on whether or not it was (1) not already part of VA routine care nationwide; (2) can be matched to 1 or more VA quality metrics/indicators; and (3) currently in use at a health care facility (innovative VA colleagues nationwide and by non-VA health care organizations).

Evaluation

After each LS, VA CCC evaluation data were collected using standardized 5-point Likert scale questions.

Results

Industrial engineers provided > 1,200 days of on-site support across the 60 teams and built 63 flow maps and 47 customized tools based on the team’s requests throughout the implementation period. Throughout the 3-phase CCC, the IEs developed standardized measurement and tracking tools for each cancer type (lung, colorectal, prostate, head and neck, and HCC). Outcomes included the sharing of best practices that spread across programs (uploaded to the national QITS site, available only to VA employees); as well as enterprisewide development of the special interest group (eg, VHA survivorship), which led to a national survivorship toolkit.

The table illustrates the overall collaborative impact across the CCC. In phase 1, 78% of the 64 aims (breast, CRC, lung, prostate) were met at 18 facilities. In phase 2, 72% of the 94 aims (CRC; HCC; and head and neck, lung, and prostate cancers) were met at 21 facilities. In phase 3, 47% of the 64 aims for head and neck and lung cancer were met at 11 facilities. The difference in the percentage of aims met during each phase was due to the variations in complexity of cancer types as well as additional logistic barriers at each institution.

Discussion

Overall, the CCC had a positive impact that improved timeliness, accessibility, and quality of the cancer care process in participating VAMCs. The majority of VAMCs focused on optimizing the lung cancer care process in all the phases of the collaborative, given that lung cancer suspicion-to-treatment process is highly complex, requiring multiple departments to coordinate workup and care, leading to the greatest room for improvement.

Industrial engineers introduced a variety of approaches to improvement to the collaborative teams, and they were integral to the development of standardized measurement and tracking tools for each type of cancer, introducing advanced SR methods for specific aims and performing appropriate data analysis. The ability of the VA system to recognize where improvements were needed was complemented by the efforts of VA clinicians and administration with direction from VERC IEs and their toolkits. Improvements were made, sometimes decreasing time from diagnosis to treatment by 50%. The VA facilities were encouraged to sustain this improvement using the toolkits with continued data gathering and implementation. In phase 1, lung cancer improvements included (1) establishing the multidisciplinary clinic, multidisciplinary rounds, and improved communication among key service lines; (2) developing a database (measurement tool) to prospectively track all cancer patients; (3) scheduling weekly multidisciplinary meetings to provide a mechanism to rapidly review patients and triage to appropriate pathways in the treatment algorithm; and (4) increasing physician participation, including oncologists, surgeons, radiologists, and radiation oncologists, to identify methods and process
changes that could eliminate wasteful steps and improve access for expediting diagnosis and treatment of patients with lung cancer who require surgery, chemotherapy, and/or radiation. The overall impact on time from abnormal CT to lung cancer surgery was reduced by > 5 months from 180 to 20 days. Substantial improvements were made in timeliness and reliability in caring for veterans with lung cancer.¹²

Groundbreaking work and exceptional results continued in the second phase for lung cancer care. In addition, the creation of a prostate cancer care web-based clinical measurement tool helped to improve the ability to proactively manage patients. The tool included same-day scheduling of biopsy and urology appointments for veterans with possible prostate cancer and the development of a protocol for expedited high-risk patients with metastatic disease. Ultimately, the wait time from urology consult to diagnosis was cut from 96 to 46 days for veterans with prostate cancer (Figure 4).

 

Once the face-to-face CCC process was established, tested, refined, and replicated successfully, the virtual team proved to be a cost-effective model. The virtual team did not travel to LSs, a major source of expense, so a process was set in place for their participation in all other facets of the collaborative. This led to the pilot testing of national virtual collaboratives (eg, specialty and surgical care collaboratives).

The toolkits for lung and CRC (phases 1 and 2) were organized, standardized, and disseminated throughout the VA to provide specific knowledge and tools to improve cancer care. The content of toolkits was primarily developed and/or identified by CCC participants. Funding for the toolkits was secured by OQP and SR, which led to the creation of the integration and crosswalk documents (eAppendix 7, available at fedprac.com/AVAHO).

In phase 3, lung cancer care teams showed the most improvement among all 3 phases of the collaborative. Aims statements in lung cancer process showed an increased percentage of improvement in all phases. Weekly multidisciplinary meetings provided a mechanism to rapidly review patients and triage appropriate pathways in the treatment algorithm. Open communication among sites and disciplines was vital and increased participation by physicians to identify ways to expedite diagnosis and treatment of lung cancer. In addition to access and timeliness of care (accommodating patients’ preference for scheduling), the teams identified areas they deemed important for successful programs and developed advisory panels that focused on quality, such as tumor boards, clinical trials, patient education, cancer care coordinator/navigator, survivorship, standard order sets and progress notes, reliable handoff, chemotherapy and radiation make/buy tools, head and neck toolkit, clinical documentation, chemotherapy efficiency, and Veterans Equitable Resource Allocation recovery for metastatic cancer.

Based on the evaluation results, participants gave their highest average ratings to items that asked about the general potential of SR to improve patient care and patient satisfaction, team dynamics, site leadership support; confidence in self, team, and coach; and the general potential of SR to improve staff satisfaction. Participants gave their lowest ratings to questions that asked about having the necessary time and resources to implement SR initiatives at their site as well as the level of active engagement by site leadership in SR work.

 

Click here to read the digital edition.

A collaboration between clinicians and industrial engineers resulted in significant improvements in cancer screening, the development of toolkits, and more efficient care for hepatocellular carcinoma and breast, colorectal, lung, head and neck, and prostate cancers.

Cancer is one of the most common causes of premature death and disability that requires long-term follow-up surveillance and oftentimes ongoing treatment for survivors that can lead to important health, psychosocial, and economic consequences.^1-3 As life expectancy continues to rise, so does the incidence and prevalence of cancer and the number of cancer survivors.^4,5 At this time, cancer care in general is poorly coordinated, fragmented, and very complex.^6,7 Research indicates effective and high-quality cancer care in a timely fashion requires health care providers to function as a multidisciplinary team.^8-11 Thus, there is an ever-increasing need to improve the efficiency and efficacy of interventions throughout the entire cancer care continuum.

Like other cancer treatment systems, the VA faces some challenges in timeliness, surveillance, and quality of the cancer care process.^12-18 Although implementation of cancer patientcentered home care and other efforts were developed to improve delivery and efficiency of cancer care in VA and non-VA facilities, the patient continuum of care remains convoluted.^2,19-23

In 2004, the Clinical Cancer Care Collaborative (C4), a national VA program, was launched to improve timeliness, quality, access improvement, efficiency, and the “sustainability and spread” of successful programs at the VA. This program included representatives throughout the VA and encompassed cancer care coordinators (clinical nurse navigators), advisory panels, and a multidisciplinary team of clinicians.

In 2009, the VA promoted the Cancer Care Collaborative (CCC) to focus on optimizing the timeliness and quality of colorectal, breast, lung, prostate, and hematologic cancer care throughout the VA health care system. The VA Office of Systems Redesign (SR) partnered with the VA-Center for Applied Systems Engineering (VA-CASE) Veteran Engineering Resource Center (VERC), including industrial engineers (IEs) to provide their expertise and support. The CCC provided a forum to develop teams; set aims; and map, measure, analyze, and implement changes to assure timely diagnosis and initiation of evidence-based treatment and subsequently sustain the practices that led to improvements in these areas.

The CCC structure was separated into 6 distinct support areas: (1) industrial/systems engineering support; (2) informatics and clinical application support; (3) development and dissemination of improvement resource guides; (4) real-time and rapid-cycle evaluation tools and approaches; (5) application of advanced operational systems engineering techniques, such as simulation and modeling to inform further system optimization; and (6) advisory panels focused on quality topics that were identified, developed, implemented, and evaluated by the participants with support from the CCC faculty.

Here the authors describe the framework of the CCC model developed by VA-CASE, demonstrate the performance improvement results of teams focusing on several types of cancer, and highlight the key indicators to best practices.

Methods

Figure 1 outlines the CCC 3-Phase Conceptual Model. Phase 1 included diagnosis (screening and symptoms); phase 2 included treatment (from diagnosis to beyond treatment); and phase 3 was designed for hub and spoke facilities where screening/diagnosis occurs in a smaller (spoke) facility and treatment occurs in the larger (hub) facility.

In the first phase, 18 facility-based teams were selected through an application and interview process and immediately applied SR to their team’s specific improvement projects, which included the following cancers: breast, colorectal, lung, and prostate.

In addition to the cancer types covered in the initial phase, phase 2 also included hepatocellular carcinoma (HCC) and head and neck cancers. National VHA Toolkits were products that developed from and for use in lung and colorectal cancers (CRCs) (phases 1 and 2). These were organized and disseminated throughout the entire VA, offering specific knowledge and tools that could be applied to improving cancer care. The toolkit included guidance documents, specific process examples, and items that could be downloaded into Microsoft SharePoint (Redmond, WA) for adaptation and use by VA facilities. The toolkit contents were primarily developed and/or identified by CCC participants and funded by the VA Office of Quality and Performance (OQP) and SR. The toolkits included links to the following resources for each cancer type in phase 2: quality indicators, tool tables, timeliness measures, understanding the continuum of care, and a resource entitled, “How Can the Quality Metrics Help Me?” (eAppendix 1, available at fedprac.com/AVAHO).

Document

fp-34-5s-s50_eappendix_1.pdf

Media Folder

Media Root

 

The phase 3 collaborative was designed for hub and spoke facilities by focusing on current state vs ideal state processes, communication patterns, and care coordination between the hub and spoke facilities. There were 10 facilities in which all teams focused on lung cancer. Each facility was made up of 1 hub and had the ability to send up to 8 participants (from either the hub or the spoke facility) to the CCC workgroup meetings. Participants were specialists, radiologists, primary care providers, pathologists, nurses, nurse practitioners, or physician assistants.

Conceptual Model Deployment

The deployment of the CCC 3-phase conceptual model was based on the Institute for Healthcare Improvement (IHI) Breakthrough Series Collaborative Model.^24,25 Implementation was carried out over 3 phases (2005-2011) after proper teaching, coaching, and learning sessions (LS)

Each LS incorporated instruction in basic systems engineering and Lean Six Sigma principles (an approach to quality improvement that focuses on reducing waste and variability) with practical, health care-based examples, case studies, and immediate application of the VA-TAMMCS (vision/analyze, team/aim, map, measure, change, sustain) SR organizational framework (Figure 2), tools, and methodologies to the process under investigation.²⁶ The VA-TAMMCS (eAppendix 2, available at fedprac.com/AVAHO) 

Document

fp-34-5s-s42_eappendix_2.pdf

Media Folder

Media Root

was developed by the VA Office of SR to improve the care provided to veterans at VA facilities.²⁷ Between LSs, teams worked to test and refine existing and innovative improvements in their systems, and the teams shared the results of their improvement efforts in monthly reports in action periods.

The CCC encouraged joint facilitation. A SR clinical coach, a VERC IE, and participating facilities were required to work together intensively (mentor and support) for 10 to 12 months. The mix of clinicians and engineers helped the facilities by bringing in diverse perspectives, which led to better decisions in the improvement of cancer care.²⁸ During the CCC, the IEs partnered with and supported clinicians, using Lean Six Sigma and SR tools and approaches to health care quality improvement to quickly make improvements in efficiency and quality (eAppendix 3a, 3b, and 3c, available at fedprac.com/AVAHO).^26,29

  

The IEs provided on-site support at all participating VAMCs during all 3 phases by providing the clinical teams with a variety of VA-TAMMCS process improvement tools to support the analysis and improvement of their organizations.

Data Collection

As part of the overall improvement process, the facilities worked on several aim statements in order to improve a primary constraint; such as timeliness and quality of care. An aim statement communicates what you want to do (eg, reduce, improve, or eliminate), by how much, and when. In order to improve timeliness, the CCC focused on measures from first evidence to tissue diagnosis, from diagnosis to treatment, and also intermediate measures, such as time from positron emission tomography scan ordered to completion. While working on overall quality of care unique to cancer, the CCC focused on measures related to documentation compliance and consistency of care provided to patients.

Phase 1

Facilities were to optimize their process (time from initial suspicion to diagnosis). Hence, participating facilities were allowed to simply identify their aim statement and pick and choose the area of focus.

Phases 2 and 3

Timeliness Aims. These aims were addressed through improvements in information technology in the Computerized Patient Record System (CPRS) electronic medical record by creating electronic order sets containing codes that alert providers daily to retrieve and follow up on abnormal test results. Primary care physicians and front desk staff also were educated on the use of these order sets and to schedule a follow-up test or specialist consult within 3 to 7 days.

Aim 1: Reduce to 15 days the time from initial suspicion to diagnosis within 1 year.
Aim 2: Reduce to 30 days the time from diagnosis to start of treatment within 1 year.

Quality Aim. Improve the compliance rate of identified quality indicators to 100% within 1 year.

Measurement Tools

The Cancer Care Measurement Tools were designed to support the OQP performance metric (Figure 3). The OQP creates and collects data on evidence-based national benchmarks to measure the quality of preventive and therapeutic health care services at the facility, VISN, and national levels. These metrics may be performance measures, performance monitors, quality indicators, and special studies, among other measures, to support clinicians, managers, and employees in improving care to veterans.

 

For each of the 3 CCC phases, VA-CASE IEs facilitated the development of standardized measurement and tracking tools for each cancer type. The tools identified key timeliness and quality measures as a function of entered patient data (eAppendixes 4-7, available at fedprac.com/AVAHO). 

Document

fp-34-5s-s42_eappendix_4.pdf

Media Folder

Media Root

Document

fp-34-5s-s42_eappendix_5.pdf

Media Folder

Media Root

 

Document

fp-34-5s-s42_eappendix_6.pdf

Media Folder

Media Root

 

Document

fp-34-5s-s42_eappendix_7.pdf

Media Folder

Media Root

Each type of cancer tool contains data entry, measurement, and chart sheets. The users entered information in the data entry sheet, and measurements and charts were automatically generated. Charts were used during the CCC LSs to identify process constraints and bottlenecks as well as quality of care issues.

Quality Improvement Toolkit Series

The Quality Improvement Toolkit Series (QITS) was created for VA clinical managers and policy makers to improve diagnosis, treatment, and patient outcomes for high-priority conditions. The goal of the QITS is to serve as the cancer care improvement resource guide to produce and disseminate the National Quality Improvement Toolkit resource.

Each tool included in the QITS is matched to 1 or more metrics of the OQP (such as a performance measure or quality indicator). For example, the types of tools include CPRS order sets and templates, enhanced registries and patient databases, service agreements, and care process flow maps. Each toolkit served as a resource for improving facility performance on a specific set of established performance measures and/or quality indicators. Toolkits that helped VA facilities improve performance on OQP quality indicators and performance measures were based on the VA-TAMMCS model and continuous improvement that was tailored to the structure and needs of the VA system. The VA-CASE staff provided guidance on the criteria for inclusion in the toolkits to promote best practice and quality in clinical practice. The criteria used by a condition-specific expert panel were based on whether or not it was (1) not already part of VA routine care nationwide; (2) can be matched to 1 or more VA quality metrics/indicators; and (3) currently in use at a health care facility (innovative VA colleagues nationwide and by non-VA health care organizations).

Evaluation

After each LS, VA CCC evaluation data were collected using standardized 5-point Likert scale questions.

Results

Industrial engineers provided > 1,200 days of on-site support across the 60 teams and built 63 flow maps and 47 customized tools based on the team’s requests throughout the implementation period. Throughout the 3-phase CCC, the IEs developed standardized measurement and tracking tools for each cancer type (lung, colorectal, prostate, head and neck, and HCC). Outcomes included the sharing of best practices that spread across programs (uploaded to the national QITS site, available only to VA employees); as well as enterprisewide development of the special interest group (eg, VHA survivorship), which led to a national survivorship toolkit.

The table illustrates the overall collaborative impact across the CCC. In phase 1, 78% of the 64 aims (breast, CRC, lung, prostate) were met at 18 facilities. In phase 2, 72% of the 94 aims (CRC; HCC; and head and neck, lung, and prostate cancers) were met at 21 facilities. In phase 3, 47% of the 64 aims for head and neck and lung cancer were met at 11 facilities. The difference in the percentage of aims met during each phase was due to the variations in complexity of cancer types as well as additional logistic barriers at each institution.

Discussion

Overall, the CCC had a positive impact that improved timeliness, accessibility, and quality of the cancer care process in participating VAMCs. The majority of VAMCs focused on optimizing the lung cancer care process in all the phases of the collaborative, given that lung cancer suspicion-to-treatment process is highly complex, requiring multiple departments to coordinate workup and care, leading to the greatest room for improvement.

Industrial engineers introduced a variety of approaches to improvement to the collaborative teams, and they were integral to the development of standardized measurement and tracking tools for each type of cancer, introducing advanced SR methods for specific aims and performing appropriate data analysis. The ability of the VA system to recognize where improvements were needed was complemented by the efforts of VA clinicians and administration with direction from VERC IEs and their toolkits. Improvements were made, sometimes decreasing time from diagnosis to treatment by 50%. The VA facilities were encouraged to sustain this improvement using the toolkits with continued data gathering and implementation. In phase 1, lung cancer improvements included (1) establishing the multidisciplinary clinic, multidisciplinary rounds, and improved communication among key service lines; (2) developing a database (measurement tool) to prospectively track all cancer patients; (3) scheduling weekly multidisciplinary meetings to provide a mechanism to rapidly review patients and triage to appropriate pathways in the treatment algorithm; and (4) increasing physician participation, including oncologists, surgeons, radiologists, and radiation oncologists, to identify methods and process
changes that could eliminate wasteful steps and improve access for expediting diagnosis and treatment of patients with lung cancer who require surgery, chemotherapy, and/or radiation. The overall impact on time from abnormal CT to lung cancer surgery was reduced by > 5 months from 180 to 20 days. Substantial improvements were made in timeliness and reliability in caring for veterans with lung cancer.¹²

Groundbreaking work and exceptional results continued in the second phase for lung cancer care. In addition, the creation of a prostate cancer care web-based clinical measurement tool helped to improve the ability to proactively manage patients. The tool included same-day scheduling of biopsy and urology appointments for veterans with possible prostate cancer and the development of a protocol for expedited high-risk patients with metastatic disease. Ultimately, the wait time from urology consult to diagnosis was cut from 96 to 46 days for veterans with prostate cancer (Figure 4).

 

Once the face-to-face CCC process was established, tested, refined, and replicated successfully, the virtual team proved to be a cost-effective model. The virtual team did not travel to LSs, a major source of expense, so a process was set in place for their participation in all other facets of the collaborative. This led to the pilot testing of national virtual collaboratives (eg, specialty and surgical care collaboratives).

The toolkits for lung and CRC (phases 1 and 2) were organized, standardized, and disseminated throughout the VA to provide specific knowledge and tools to improve cancer care. The content of toolkits was primarily developed and/or identified by CCC participants. Funding for the toolkits was secured by OQP and SR, which led to the creation of the integration and crosswalk documents (eAppendix 7, available at fedprac.com/AVAHO).

In phase 3, lung cancer care teams showed the most improvement among all 3 phases of the collaborative. Aims statements in lung cancer process showed an increased percentage of improvement in all phases. Weekly multidisciplinary meetings provided a mechanism to rapidly review patients and triage appropriate pathways in the treatment algorithm. Open communication among sites and disciplines was vital and increased participation by physicians to identify ways to expedite diagnosis and treatment of lung cancer. In addition to access and timeliness of care (accommodating patients’ preference for scheduling), the teams identified areas they deemed important for successful programs and developed advisory panels that focused on quality, such as tumor boards, clinical trials, patient education, cancer care coordinator/navigator, survivorship, standard order sets and progress notes, reliable handoff, chemotherapy and radiation make/buy tools, head and neck toolkit, clinical documentation, chemotherapy efficiency, and Veterans Equitable Resource Allocation recovery for metastatic cancer.

Based on the evaluation results, participants gave their highest average ratings to items that asked about the general potential of SR to improve patient care and patient satisfaction, team dynamics, site leadership support; confidence in self, team, and coach; and the general potential of SR to improve staff satisfaction. Participants gave their lowest ratings to questions that asked about having the necessary time and resources to implement SR initiatives at their site as well as the level of active engagement by site leadership in SR work.

 

Click here to read the digital edition.

A collaboration between clinicians and industrial engineers resulted in significant improvements in cancer screening, the development of toolkits, and more efficient care for hepatocellular carcinoma and breast, colorectal, lung, head and neck, and prostate cancers.

Cancer is one of the most common causes of premature death and disability that requires long-term follow-up surveillance and oftentimes ongoing treatment for survivors that can lead to important health, psychosocial, and economic consequences.^1-3 As life expectancy continues to rise, so does the incidence and prevalence of cancer and the number of cancer survivors.^4,5 At this time, cancer care in general is poorly coordinated, fragmented, and very complex.^6,7 Research indicates effective and high-quality cancer care in a timely fashion requires health care providers to function as a multidisciplinary team.^8-11 Thus, there is an ever-increasing need to improve the efficiency and efficacy of interventions throughout the entire cancer care continuum.

Like other cancer treatment systems, the VA faces some challenges in timeliness, surveillance, and quality of the cancer care process.^12-18 Although implementation of cancer patientcentered home care and other efforts were developed to improve delivery and efficiency of cancer care in VA and non-VA facilities, the patient continuum of care remains convoluted.^2,19-23

In 2004, the Clinical Cancer Care Collaborative (C4), a national VA program, was launched to improve timeliness, quality, access improvement, efficiency, and the “sustainability and spread” of successful programs at the VA. This program included representatives throughout the VA and encompassed cancer care coordinators (clinical nurse navigators), advisory panels, and a multidisciplinary team of clinicians.

In 2009, the VA promoted the Cancer Care Collaborative (CCC) to focus on optimizing the timeliness and quality of colorectal, breast, lung, prostate, and hematologic cancer care throughout the VA health care system. The VA Office of Systems Redesign (SR) partnered with the VA-Center for Applied Systems Engineering (VA-CASE) Veteran Engineering Resource Center (VERC), including industrial engineers (IEs) to provide their expertise and support. The CCC provided a forum to develop teams; set aims; and map, measure, analyze, and implement changes to assure timely diagnosis and initiation of evidence-based treatment and subsequently sustain the practices that led to improvements in these areas.

The CCC structure was separated into 6 distinct support areas: (1) industrial/systems engineering support; (2) informatics and clinical application support; (3) development and dissemination of improvement resource guides; (4) real-time and rapid-cycle evaluation tools and approaches; (5) application of advanced operational systems engineering techniques, such as simulation and modeling to inform further system optimization; and (6) advisory panels focused on quality topics that were identified, developed, implemented, and evaluated by the participants with support from the CCC faculty.

Here the authors describe the framework of the CCC model developed by VA-CASE, demonstrate the performance improvement results of teams focusing on several types of cancer, and highlight the key indicators to best practices.

Methods

Figure 1 outlines the CCC 3-Phase Conceptual Model. Phase 1 included diagnosis (screening and symptoms); phase 2 included treatment (from diagnosis to beyond treatment); and phase 3 was designed for hub and spoke facilities where screening/diagnosis occurs in a smaller (spoke) facility and treatment occurs in the larger (hub) facility.

In the first phase, 18 facility-based teams were selected through an application and interview process and immediately applied SR to their team’s specific improvement projects, which included the following cancers: breast, colorectal, lung, and prostate.

In addition to the cancer types covered in the initial phase, phase 2 also included hepatocellular carcinoma (HCC) and head and neck cancers. National VHA Toolkits were products that developed from and for use in lung and colorectal cancers (CRCs) (phases 1 and 2). These were organized and disseminated throughout the entire VA, offering specific knowledge and tools that could be applied to improving cancer care. The toolkit included guidance documents, specific process examples, and items that could be downloaded into Microsoft SharePoint (Redmond, WA) for adaptation and use by VA facilities. The toolkit contents were primarily developed and/or identified by CCC participants and funded by the VA Office of Quality and Performance (OQP) and SR. The toolkits included links to the following resources for each cancer type in phase 2: quality indicators, tool tables, timeliness measures, understanding the continuum of care, and a resource entitled, “How Can the Quality Metrics Help Me?” (eAppendix 1, available at fedprac.com/AVAHO).

Document

fp-34-5s-s50_eappendix_1.pdf

Media Folder

Media Root

 

The phase 3 collaborative was designed for hub and spoke facilities by focusing on current state vs ideal state processes, communication patterns, and care coordination between the hub and spoke facilities. There were 10 facilities in which all teams focused on lung cancer. Each facility was made up of 1 hub and had the ability to send up to 8 participants (from either the hub or the spoke facility) to the CCC workgroup meetings. Participants were specialists, radiologists, primary care providers, pathologists, nurses, nurse practitioners, or physician assistants.

Conceptual Model Deployment

The deployment of the CCC 3-phase conceptual model was based on the Institute for Healthcare Improvement (IHI) Breakthrough Series Collaborative Model.^24,25 Implementation was carried out over 3 phases (2005-2011) after proper teaching, coaching, and learning sessions (LS)

Each LS incorporated instruction in basic systems engineering and Lean Six Sigma principles (an approach to quality improvement that focuses on reducing waste and variability) with practical, health care-based examples, case studies, and immediate application of the VA-TAMMCS (vision/analyze, team/aim, map, measure, change, sustain) SR organizational framework (Figure 2), tools, and methodologies to the process under investigation.²⁶ The VA-TAMMCS (eAppendix 2, available at fedprac.com/AVAHO) 

Document

fp-34-5s-s42_eappendix_2.pdf

Media Folder

Media Root

was developed by the VA Office of SR to improve the care provided to veterans at VA facilities.²⁷ Between LSs, teams worked to test and refine existing and innovative improvements in their systems, and the teams shared the results of their improvement efforts in monthly reports in action periods.

The CCC encouraged joint facilitation. A SR clinical coach, a VERC IE, and participating facilities were required to work together intensively (mentor and support) for 10 to 12 months. The mix of clinicians and engineers helped the facilities by bringing in diverse perspectives, which led to better decisions in the improvement of cancer care.²⁸ During the CCC, the IEs partnered with and supported clinicians, using Lean Six Sigma and SR tools and approaches to health care quality improvement to quickly make improvements in efficiency and quality (eAppendix 3a, 3b, and 3c, available at fedprac.com/AVAHO).^26,29

  

The IEs provided on-site support at all participating VAMCs during all 3 phases by providing the clinical teams with a variety of VA-TAMMCS process improvement tools to support the analysis and improvement of their organizations.

Data Collection

As part of the overall improvement process, the facilities worked on several aim statements in order to improve a primary constraint; such as timeliness and quality of care. An aim statement communicates what you want to do (eg, reduce, improve, or eliminate), by how much, and when. In order to improve timeliness, the CCC focused on measures from first evidence to tissue diagnosis, from diagnosis to treatment, and also intermediate measures, such as time from positron emission tomography scan ordered to completion. While working on overall quality of care unique to cancer, the CCC focused on measures related to documentation compliance and consistency of care provided to patients.

Phase 1

Facilities were to optimize their process (time from initial suspicion to diagnosis). Hence, participating facilities were allowed to simply identify their aim statement and pick and choose the area of focus.

Phases 2 and 3

Timeliness Aims. These aims were addressed through improvements in information technology in the Computerized Patient Record System (CPRS) electronic medical record by creating electronic order sets containing codes that alert providers daily to retrieve and follow up on abnormal test results. Primary care physicians and front desk staff also were educated on the use of these order sets and to schedule a follow-up test or specialist consult within 3 to 7 days.

Aim 1: Reduce to 15 days the time from initial suspicion to diagnosis within 1 year.
Aim 2: Reduce to 30 days the time from diagnosis to start of treatment within 1 year.

Quality Aim. Improve the compliance rate of identified quality indicators to 100% within 1 year.

Measurement Tools

The Cancer Care Measurement Tools were designed to support the OQP performance metric (Figure 3). The OQP creates and collects data on evidence-based national benchmarks to measure the quality of preventive and therapeutic health care services at the facility, VISN, and national levels. These metrics may be performance measures, performance monitors, quality indicators, and special studies, among other measures, to support clinicians, managers, and employees in improving care to veterans.

 

For each of the 3 CCC phases, VA-CASE IEs facilitated the development of standardized measurement and tracking tools for each cancer type. The tools identified key timeliness and quality measures as a function of entered patient data (eAppendixes 4-7, available at fedprac.com/AVAHO). 

Document

fp-34-5s-s42_eappendix_4.pdf

Media Folder

Media Root

Document

fp-34-5s-s42_eappendix_5.pdf

Media Folder

Media Root

 

Document

fp-34-5s-s42_eappendix_6.pdf

Media Folder

Media Root

 

Document

fp-34-5s-s42_eappendix_7.pdf

Media Folder

Media Root

Each type of cancer tool contains data entry, measurement, and chart sheets. The users entered information in the data entry sheet, and measurements and charts were automatically generated. Charts were used during the CCC LSs to identify process constraints and bottlenecks as well as quality of care issues.

Quality Improvement Toolkit Series

The Quality Improvement Toolkit Series (QITS) was created for VA clinical managers and policy makers to improve diagnosis, treatment, and patient outcomes for high-priority conditions. The goal of the QITS is to serve as the cancer care improvement resource guide to produce and disseminate the National Quality Improvement Toolkit resource.

Each tool included in the QITS is matched to 1 or more metrics of the OQP (such as a performance measure or quality indicator). For example, the types of tools include CPRS order sets and templates, enhanced registries and patient databases, service agreements, and care process flow maps. Each toolkit served as a resource for improving facility performance on a specific set of established performance measures and/or quality indicators. Toolkits that helped VA facilities improve performance on OQP quality indicators and performance measures were based on the VA-TAMMCS model and continuous improvement that was tailored to the structure and needs of the VA system. The VA-CASE staff provided guidance on the criteria for inclusion in the toolkits to promote best practice and quality in clinical practice. The criteria used by a condition-specific expert panel were based on whether or not it was (1) not already part of VA routine care nationwide; (2) can be matched to 1 or more VA quality metrics/indicators; and (3) currently in use at a health care facility (innovative VA colleagues nationwide and by non-VA health care organizations).

Evaluation

After each LS, VA CCC evaluation data were collected using standardized 5-point Likert scale questions.

Results

Industrial engineers provided > 1,200 days of on-site support across the 60 teams and built 63 flow maps and 47 customized tools based on the team’s requests throughout the implementation period. Throughout the 3-phase CCC, the IEs developed standardized measurement and tracking tools for each cancer type (lung, colorectal, prostate, head and neck, and HCC). Outcomes included the sharing of best practices that spread across programs (uploaded to the national QITS site, available only to VA employees); as well as enterprisewide development of the special interest group (eg, VHA survivorship), which led to a national survivorship toolkit.

The table illustrates the overall collaborative impact across the CCC. In phase 1, 78% of the 64 aims (breast, CRC, lung, prostate) were met at 18 facilities. In phase 2, 72% of the 94 aims (CRC; HCC; and head and neck, lung, and prostate cancers) were met at 21 facilities. In phase 3, 47% of the 64 aims for head and neck and lung cancer were met at 11 facilities. The difference in the percentage of aims met during each phase was due to the variations in complexity of cancer types as well as additional logistic barriers at each institution.

Discussion

Overall, the CCC had a positive impact that improved timeliness, accessibility, and quality of the cancer care process in participating VAMCs. The majority of VAMCs focused on optimizing the lung cancer care process in all the phases of the collaborative, given that lung cancer suspicion-to-treatment process is highly complex, requiring multiple departments to coordinate workup and care, leading to the greatest room for improvement.

Industrial engineers introduced a variety of approaches to improvement to the collaborative teams, and they were integral to the development of standardized measurement and tracking tools for each type of cancer, introducing advanced SR methods for specific aims and performing appropriate data analysis. The ability of the VA system to recognize where improvements were needed was complemented by the efforts of VA clinicians and administration with direction from VERC IEs and their toolkits. Improvements were made, sometimes decreasing time from diagnosis to treatment by 50%. The VA facilities were encouraged to sustain this improvement using the toolkits with continued data gathering and implementation. In phase 1, lung cancer improvements included (1) establishing the multidisciplinary clinic, multidisciplinary rounds, and improved communication among key service lines; (2) developing a database (measurement tool) to prospectively track all cancer patients; (3) scheduling weekly multidisciplinary meetings to provide a mechanism to rapidly review patients and triage to appropriate pathways in the treatment algorithm; and (4) increasing physician participation, including oncologists, surgeons, radiologists, and radiation oncologists, to identify methods and process
changes that could eliminate wasteful steps and improve access for expediting diagnosis and treatment of patients with lung cancer who require surgery, chemotherapy, and/or radiation. The overall impact on time from abnormal CT to lung cancer surgery was reduced by > 5 months from 180 to 20 days. Substantial improvements were made in timeliness and reliability in caring for veterans with lung cancer.¹²

Groundbreaking work and exceptional results continued in the second phase for lung cancer care. In addition, the creation of a prostate cancer care web-based clinical measurement tool helped to improve the ability to proactively manage patients. The tool included same-day scheduling of biopsy and urology appointments for veterans with possible prostate cancer and the development of a protocol for expedited high-risk patients with metastatic disease. Ultimately, the wait time from urology consult to diagnosis was cut from 96 to 46 days for veterans with prostate cancer (Figure 4).

 

Once the face-to-face CCC process was established, tested, refined, and replicated successfully, the virtual team proved to be a cost-effective model. The virtual team did not travel to LSs, a major source of expense, so a process was set in place for their participation in all other facets of the collaborative. This led to the pilot testing of national virtual collaboratives (eg, specialty and surgical care collaboratives).

The toolkits for lung and CRC (phases 1 and 2) were organized, standardized, and disseminated throughout the VA to provide specific knowledge and tools to improve cancer care. The content of toolkits was primarily developed and/or identified by CCC participants. Funding for the toolkits was secured by OQP and SR, which led to the creation of the integration and crosswalk documents (eAppendix 7, available at fedprac.com/AVAHO).

In phase 3, lung cancer care teams showed the most improvement among all 3 phases of the collaborative. Aims statements in lung cancer process showed an increased percentage of improvement in all phases. Weekly multidisciplinary meetings provided a mechanism to rapidly review patients and triage appropriate pathways in the treatment algorithm. Open communication among sites and disciplines was vital and increased participation by physicians to identify ways to expedite diagnosis and treatment of lung cancer. In addition to access and timeliness of care (accommodating patients’ preference for scheduling), the teams identified areas they deemed important for successful programs and developed advisory panels that focused on quality, such as tumor boards, clinical trials, patient education, cancer care coordinator/navigator, survivorship, standard order sets and progress notes, reliable handoff, chemotherapy and radiation make/buy tools, head and neck toolkit, clinical documentation, chemotherapy efficiency, and Veterans Equitable Resource Allocation recovery for metastatic cancer.

Based on the evaluation results, participants gave their highest average ratings to items that asked about the general potential of SR to improve patient care and patient satisfaction, team dynamics, site leadership support; confidence in self, team, and coach; and the general potential of SR to improve staff satisfaction. Participants gave their lowest ratings to questions that asked about having the necessary time and resources to implement SR initiatives at their site as well as the level of active engagement by site leadership in SR work.

 

Click here to read the digital edition.

From 1982 to 2011, the melanoma incidence rate doubled in the US.¹ In 2018, an estimated 87,290 cases of melanoma in situ and 91,270 cases of invasive melanoma will be diagnosed in the US, and 9,320 deaths will be attributable to melanoma.² Early detection of melanoma is critically important to reduce melanoma-related mortality, with 5-year survival rates as high as 97% at stage 1A vs a 20% 5-year survival when there is distant metastasis.^2,3 Melanoma is particularly relevant for medical providers working with veterans because melanoma disproportionately affects service members with an incidence rate ratio of 1.62 (95% confidence interval [CI], 1.40-1.86) compared with that of the general population.⁴

Biopsy is the definitive diagnostic tool for melanoma. Histologic analysis differentiates melanoma from seborrheic keratoses, pigmented nevi, dermatofibromas, and other pigmented lesions that can resemble melanoma on clinical examination. However, biopsy must be used judiciously as unnecessary biopsies contribute to health care costs and leave scars, which can have psychosocial implications. With benign nevi outnumbering melanoma about 2 million to 1, biopsy is indicated once a threshold of suspicion is obtained.⁵

Dermoscopic Tool

Dermoscopy is a microscopy-based tool to improve noninvasive diagnostic discrimination of skin lesions based on color and structure analysis. Coloration provides an indication of the composition of elements present in the skin with keratin appearing yellow, blood appearing red, and collagen appearing white. Coloration also suggests pigment depth as melanin appears black when located in the stratum corneum, brown when located deeper in the epidermis, and blue when located in the dermis.⁶ Finally, characteristic histopathologic alterations in the dermoepidermal junction, rete ridges, pigment-containing cells, and/or melanocyte granules that occur in melanoma are recognizable with dermoscopy.⁶

In 2001, Bafounta and colleagues performed the first meta-analysis on the efficacy of dermoscopy compared with that of clinical evaluation, finding that dermoscopy performed specifically by dermatology-trained clinicians improved the accuracy of identifying melanoma from an odds ratio of 16 (95% CI, 9-31) with naked eye examination to 76 (95% CI, 25-223) with dermoscopy.⁷

More recently, Terushkin and colleagues demonstrated that diagnosis specificity improves when a general dermatologist is trained in dermoscopic pattern recognition. Naked eye examination produced a benign to malignant ratio (BMR) of 18.4:1, indicating that about 18 of 19 biopsies considered suspicious for melanoma ultimately yielded benign melanocytic lesions. Although the BMR for the general dermatologist experienced an increase after dermoscopy training, the ratio eventually decreased to 7.9:1.⁸

Dermoscopic Analysis

Some of the common patterns recognized in melanoma are demonstrated in Figures 1 and 2. Figure 1 is a close-up of a patient’s upper back showing a solitary asymmetric melanocytic lesion containing multiple red, brown, black, and blue hues. 

The lesion is highly suspicious for melanoma. Key patterns identified under dermoscopy in Figure 2 increase the level of suspicion. The pink circle in the upper left of the lesion demonstrates a scarlike regression of pigment structure. 

The orange triangle signifies a region with marked variability in color called an atypical pigment network, and the centrally located yellow circle and gray square identify interspersed atypical dots and globules of color. The red rectangle on the right highlights irregular streaking, linear radial projections suggestive of superficial spreading melanoma. The green line identifies hypopigmentation with surrounding curvilinear globular structures collectively known as a negative network.  Finally, the bottom blue triangle overlies an area with a hazy blue tinge called a blue-white veil, indicating the presence of melanocytes deep in the dermis (Table 1).^6,9

Pattern analysis, the dermoscopic interpretation method preferred by pigmented lesion specialists, requires simultaneously assessing numerous lesion patterns that vary depending on body site.¹⁰ Alternative dermoscopic algorithms that focus on the most common features of melanoma have been developed to aid practitioners with the interpretation of dermoscopy findings: the 7-point checklist, the Menzies method, the ABCD rule, and the CASH algorithm (Tables 2, 3, 4, and 5). 

To apply these algorithms to evaluate the lesion in Figures 1 and 2 ( eAppendix

Document

fp-35-5s-s39_eappendix.pdf

Media Folder

Media Root

).^11-14 The triage amalgamated dermoscopic algorithm (TADA) method, a newer algorithm designed for novice dermoscopy users, is also discussed briefly.

Argenziano and colleagues developed the 7-point checklist in 1998. Two points are assigned to the lesion for each of the major criteria and 1 point for each minor criteria. 

The major criteria include an atypical pigment network, blue-white veil, and atypical vascular pattern; the minor criteria include irregular streaks, irregular pigmentation, irregular dots/globules, and regression structures.¹¹ The lesion shown in Figure 2 scores an 8 out of 10 by this metric, handily surpassing the 3 points required to suggest melanoma.¹¹

The Menzies method was developed by Menzies and colleagues in 1996. To be classified as melanoma, the pigmented lesion must show an absence of pattern symmetry and color uniformity while simultaneously exhibiting at least one of the following: blue-white veil, multiple brown dots, pseudopods, radial streaming, scarlike depigmentation, peripheral block dots/globules, 5 to 6 colors, multiple blue/gray dots, and a broadened network.¹² 

Again, the lesion shown in Figure 2 meets the criteria concerning for melanoma based on this algorithm.

The ABCD rule is a more technical dermoscopic evaluation algorithm developed in 1994 by Stolz and colleagues. This method yields a numeric value called the total dermoscopic score (TDS) based on Asymmetry, Border pigment pattern, Color variation, and 5 Different structural components. 

The assessment of asymmetry is determined by analyzing the lesion in a plane bisected by 2 axes set at 90°. A score from 0 to 2 is assigned based on the number of axes showing asymmetry in shape, color, or structure. Border pigment pattern is analyzed by dividing the lesion into eighths. A sharp, abrupt change in pigment pattern at the periphery earns the lesion 1 point for each division. The determination of the color variation score is done by adding 1 point for each white, red, light brown, dark brown, blue-gray, or black region identified in the lesion. Last, the lesion is assigned 1 point for each of 5 different structural components observed in the lesion, which include networks, homogenous areas, dots, globules, and streaks. To be significant, homogenous areas must be at least 10% of the lesion, and multiple branched streaks or dots must be visible. The TDS is calculated with the following formula: TDS = 1.3 x Asymmetry + 0.1 x Border + 0.5 x Color + 0.5 x Different. Higher scores are more concerning of melanoma, with scores > 5.45 suggesting melanoma.¹³ The lesion shown in Figure 2 registers a 7.7 by this metric.

Henning and colleagues developed the CASH algorithm in 2006 with the intention of assisting less experienced dermoscopy users with lesion evaluation.¹⁴ This algorithm tallies points for Color, Architectural disorder, Symmetry, and Homogeneity. One point is attributed to a lesion for each light brown, dark brown, black, red, white, and/or blue region present. Architectural disorder is assigned a point value between 0 and 2, with 0 indicating the absence of or minimal architectural disorder, 1 indicating moderate disorder, and 2 indicating marked disorder. Symmetry is assigned a point value between 0 and 2, with 0 points assigned to a lesion that exhibits biaxial symmetry, 1 point assigned to a lesion that exhibits monoaxial symmetry, and 2 points assigned to a lesion that exhibits biaxial asymmetry. Finally, 1 point is attributed to a lesion for evidence of each of the following: atypical network, dots/globules, streaks/pseudopods, blue-white veil, regression structures, blotches > 10% of the overall lesion size, and polymorphous blood vessels. The lesion in Figure 2 scores 16 points out of the maximum total CASH score of 17. Any lesion scoring 8 or more is suggestive of malignant melanoma.¹⁴

Finally, the TADA method was developed by Rogers and colleagues in 2016.¹⁵ This method uses sequential questions to evaluate lesions. First, “Does the lesion exhibit clear dermoscopic evidence of an angioma, dermatofibroma, or seborrheic keratosis?” If “yes,” then no additional dermoscopic evaluation is necessary, and it is recommended to monitor the lesion. If the answer to the first question is “no,” then ask, “Does the lesion exhibit architectural disorder?” The presence of architectural disorder is based on an overall impression of the lesion, which includes symmetry with regard to structures and colors. Any lesion deemed to exhibit architectural disorder should be biopsied. If the lesion has no architectural disorder, the third question is, “Does the lesion contain any starburst patterns, blue-black or gray coloration, shiny white structures, negative networks, ulcers or erosions, and/or vessels?” If “yes,” then the lesion should be biopsied. Since the lesion in Figure 2 exhibits marked architectural disorder in terms of symmetry and color, analysis of the lesion with the TADA method would yield a recommendation for biopsy.¹⁵

 

Dermoscopy in Practice

A. Bernard Ackerman, MD, a key figure in the modern era of dermatopathology, wrote an editorial for the Journal of the American Academy of Dermatology in 1985 titled “No one should die of malignant melanoma.” The editorial highlighted that the visual changes associated with melanoma often manifest years prior to malignant invasion and advocated that all physicians should have competence in melanoma detection, specifically mentioning the importance of training primary care physicians (PCPs), dermatologists, and pathologists in this regard.¹⁶ This sentiment is equally as true now as it was in 1985.

Naked eye examination paired with an evaluation of patient risk factors for melanoma, including fair skin types, significant sun exposure history, history of sunburn, geographic location, and personal and family history of melanoma, are the foundation of melanoma detection efforts.¹⁷ Studies suggest that the triage skills of PCPs could be improved in regard to the evaluation of pigmented lesions. Primary care residents, for instance, did not accurately diagnose 40% of malignant melanoma cases.^18,19 Additionally, a meta-analysis demonstrated that PCP accuracy when diagnosing malignant melanoma ranged between 49% and 80%, significantly less than the 85% to 89% exhibited by practicing dermatologists.¹⁹ Dermoscopy could be incorporated as an element of the skin examination to enhance lesion discrimination among PCPs, as it has proven use in dermatologic practice.

Dermoscopy is not readily used by PCPs. A survey study of 705 family practitioners in the US performed by Morris and colleagues demonstrated that only 8.3% of participants currently use a dermatoscope to evaluate pigmented lesions.²⁰ The most common barriers to dermoscopy use cited by PCPs in the US include the cost of the dermatoscope, the time required to acquire proficiency, and the lack of financial reimbursement.¹⁶ True utilization of dermoscopy among PCPs is higher than this figure suggests due to the number of PCPs who access dermoscopic evaluations via teledermatology. All 21 Veterans Integrated Services Networks of the Veterans Health Administration (VHA) system, for instance, participate in teledermatology and jointly employ more than 1,150 trained telehealth clinical technicians who created a collective 107,000 teledermatology encounters with and without dermoscopy for evaluation by dermatologists in the most recent fiscal year(Martin Weinstock, written communication, October 2017). Nonetheless, it is necessary to determine the contribution that wider utilization of dermoscopy among PCPs would have on melanoma surveillance.

Studies show that dermoscopic algorithms improve the sensitivity while slightly decreasing the specificity of PCPs to detect melanoma compared with that of the naked eye examination. Dolianitis and colleagues demonstrated that a baseline sensitivity of 60.9% for melanoma detection improved to 85.4% with the 7-point checklist, 85.4% with Menzies method, and 77.5% with the ABCD rule, while the baseline specificity of 85.4% moderated to 73.0%, 77.7%, and 80.4%, respectively, among 61 medical practitioners after studying dermoscopy techniques from 2 CDs.²¹ Westerhoff and colleagues performed a randomized controlled trial with 74 PCPs to determine the effect of a minimal intervention on melanoma diagnostic accuracy. The intervention consisted of providing participants in the experimental group with an atlas of microscopic features common to melanoma to be read at the participants’ leisure, a 1-hour presentation on microscopy, and a 25-questionpractice quiz. Participants randomized to the intervention group improved their diagnostic accuracy from 57.8% to 75.9% with the use of dermoscopy. This group also experiencedimproved accuracy in its clinical diagnosis of melanoma from 54.6% to 62.7%.²²

Argenziano and colleagues demonstrated similar results after PCPs attended a 4-hour workshop on dermoscopy. The 73 PCPs in this study evaluated 2,522 lesions randomized to naked eye examination or dermoscopy. The BMR, calculated from the data provided, improved from 12.6:1 to 10.5:1, respectively, when dermoscopy was incorporated into lesion analysis, while the sensitivity increased from 54.1% to 79.2% and the negative predictive value increased from 95.8% to 98.1%. It is important to note that the BMR and negative predictive value improved in tandem, indicating that PCPs were more discriminatory with their referrals for evaluation by dermatology while capturing a greater percentage of melanomas.²³

These studies are not without limitations that preclude broad generalizations. For example, Dolianitis and colleagues and Westerhoff and colleagues provided participants with dermoscopic images of the lesions to be evaluated instead of requiring personal use of a dermatoscope, whereas the study by Argenziano and colleagues incorporated only 6 histopathologically proven malignant melanomas into each of the naked eye examination and the experimental dermoscopy groups.^21-23 Yet these studies suggest that broader use of dermoscopy among PCPs could improve the accuracy of melanoma detection given clinically relevant training.

Several additional studies identify positive correlations associated with dermoscopy use among PCPs. A recent survey of 425 French general practitioners found that 8% of the study participants acknowledged owning a dermatoscope. Dermatoscope owners spent a statistically significant longer time analyzing each pigmented skin lesions, exhibited greater confidence in their analysis of pigmented lesions, and issued fewer overall referrals to dermatologists.²⁴

Similarly, Rosendahl and colleagues evaluated the number needed to treat (NNT) (equivalent to the BMR) among 193 Australian PCPs and found that the NNT was inversely correlated to the frequency with which the physicians used dermoscopy. However, it was difficult to determine the definitive cause of the reduced NNT in this study because a similar effect was observed when NNT was evaluated based on general practitioner subspecialization.²⁵ Again, despite limitations, these studies suggest that increased dermoscopy use among PCPs could reduce the morbidity of lifelong scarring as well as the short-term anxiety associated with a possible melanoma diagnosis.

 

Limitations

Even in the hands of a trained dermatologist, dermoscopy has limitations. Featureless melanoma is a term applied to melanoma lesions bereft of classical findings on both naked eye examination and dermoscopy. Menzies, a dermatologic pioneer in dermoscopy, acknowledged this limitation in 1996 while showing that 8% of melanomas evaded dermoscopic detection. He proceeded to discuss the importance of clinical history in melanoma detection because all of the featureless melanomas exhibited recent changes in size, shape, and/or color.²⁶ More recently, sequential dermoscopy (mole mapping) imaging has been implemented to successfully identify these lesions.²⁷ Thus, dermoscopy cannot replace dermatologists trained in the art of visual assessment with honed clinical diagnostic acumen. Rather, dermoscopy is a tool to enhance the assessment of clinically suspicious lesions and aid diagnostic discrimination of uncertain pigmented lesions.

Conclusion

Primary care physicians are on the frontline of medicine and often the first to have the opportunity to detect the presence of melanoma. Notably, 52.2% of the 884.7 million medical office visits performed annually in the US are with PCPs.²⁸ Despite the benefits, dermoscopy is not uniformly used by dermatologists in the US. Of dermatologists practicing for more than 20 years, 76.2% use dermoscopy compared with 97.8% of dermatologists in practice for less than 5 years. This illustrates an increased use in tandem with dermatology residencies integrating dermoscopy training as a component of the curriculum, showing the importance of incorporating dermoscopy into medical school and residency training for PCPs.^.29-31 Guidelines regarding dermoscopy training and dermoscopic evaluation algorithms should be established, routinely taught in medical education, and actively incorporated into training curriculum for PCPs in order to improve patient care and realize the potential health care savings associated with the early diagnosis and treatment of melanoma. Dermoscopic-teledermatology consultations present a viable opportunity within the VHA to expedite access to care for veterans and simultaneously offer evaluative feedback on lesions to referring PCPs, as skilled, dermoscopy-trained dermatologists render the diagnoses. Given the devastating mortality rate of melanoma, continued multidisciplinary education on identifying melanoma is of the utmost importance for patient care. Widespread implementation of dermoscopy and dermoscopic-teledermatology consultations could save lives and slow the ever-increasing economic burden associated with melanoma treatment, costing $1.467 billion in 2016.³²

From 1982 to 2011, the melanoma incidence rate doubled in the US.¹ In 2018, an estimated 87,290 cases of melanoma in situ and 91,270 cases of invasive melanoma will be diagnosed in the US, and 9,320 deaths will be attributable to melanoma.² Early detection of melanoma is critically important to reduce melanoma-related mortality, with 5-year survival rates as high as 97% at stage 1A vs a 20% 5-year survival when there is distant metastasis.^2,3 Melanoma is particularly relevant for medical providers working with veterans because melanoma disproportionately affects service members with an incidence rate ratio of 1.62 (95% confidence interval [CI], 1.40-1.86) compared with that of the general population.⁴

Biopsy is the definitive diagnostic tool for melanoma. Histologic analysis differentiates melanoma from seborrheic keratoses, pigmented nevi, dermatofibromas, and other pigmented lesions that can resemble melanoma on clinical examination. However, biopsy must be used judiciously as unnecessary biopsies contribute to health care costs and leave scars, which can have psychosocial implications. With benign nevi outnumbering melanoma about 2 million to 1, biopsy is indicated once a threshold of suspicion is obtained.⁵

Dermoscopic Tool

Dermoscopy is a microscopy-based tool to improve noninvasive diagnostic discrimination of skin lesions based on color and structure analysis. Coloration provides an indication of the composition of elements present in the skin with keratin appearing yellow, blood appearing red, and collagen appearing white. Coloration also suggests pigment depth as melanin appears black when located in the stratum corneum, brown when located deeper in the epidermis, and blue when located in the dermis.⁶ Finally, characteristic histopathologic alterations in the dermoepidermal junction, rete ridges, pigment-containing cells, and/or melanocyte granules that occur in melanoma are recognizable with dermoscopy.⁶

In 2001, Bafounta and colleagues performed the first meta-analysis on the efficacy of dermoscopy compared with that of clinical evaluation, finding that dermoscopy performed specifically by dermatology-trained clinicians improved the accuracy of identifying melanoma from an odds ratio of 16 (95% CI, 9-31) with naked eye examination to 76 (95% CI, 25-223) with dermoscopy.⁷

More recently, Terushkin and colleagues demonstrated that diagnosis specificity improves when a general dermatologist is trained in dermoscopic pattern recognition. Naked eye examination produced a benign to malignant ratio (BMR) of 18.4:1, indicating that about 18 of 19 biopsies considered suspicious for melanoma ultimately yielded benign melanocytic lesions. Although the BMR for the general dermatologist experienced an increase after dermoscopy training, the ratio eventually decreased to 7.9:1.⁸

Dermoscopic Analysis

Some of the common patterns recognized in melanoma are demonstrated in Figures 1 and 2. Figure 1 is a close-up of a patient’s upper back showing a solitary asymmetric melanocytic lesion containing multiple red, brown, black, and blue hues. 

The lesion is highly suspicious for melanoma. Key patterns identified under dermoscopy in Figure 2 increase the level of suspicion. The pink circle in the upper left of the lesion demonstrates a scarlike regression of pigment structure. 

The orange triangle signifies a region with marked variability in color called an atypical pigment network, and the centrally located yellow circle and gray square identify interspersed atypical dots and globules of color. The red rectangle on the right highlights irregular streaking, linear radial projections suggestive of superficial spreading melanoma. The green line identifies hypopigmentation with surrounding curvilinear globular structures collectively known as a negative network.  Finally, the bottom blue triangle overlies an area with a hazy blue tinge called a blue-white veil, indicating the presence of melanocytes deep in the dermis (Table 1).^6,9

Pattern analysis, the dermoscopic interpretation method preferred by pigmented lesion specialists, requires simultaneously assessing numerous lesion patterns that vary depending on body site.¹⁰ Alternative dermoscopic algorithms that focus on the most common features of melanoma have been developed to aid practitioners with the interpretation of dermoscopy findings: the 7-point checklist, the Menzies method, the ABCD rule, and the CASH algorithm (Tables 2, 3, 4, and 5). 

To apply these algorithms to evaluate the lesion in Figures 1 and 2 ( eAppendix

Document

fp-35-5s-s39_eappendix.pdf

Media Folder

Media Root

).^11-14 The triage amalgamated dermoscopic algorithm (TADA) method, a newer algorithm designed for novice dermoscopy users, is also discussed briefly.

Argenziano and colleagues developed the 7-point checklist in 1998. Two points are assigned to the lesion for each of the major criteria and 1 point for each minor criteria. 

The major criteria include an atypical pigment network, blue-white veil, and atypical vascular pattern; the minor criteria include irregular streaks, irregular pigmentation, irregular dots/globules, and regression structures.¹¹ The lesion shown in Figure 2 scores an 8 out of 10 by this metric, handily surpassing the 3 points required to suggest melanoma.¹¹

The Menzies method was developed by Menzies and colleagues in 1996. To be classified as melanoma, the pigmented lesion must show an absence of pattern symmetry and color uniformity while simultaneously exhibiting at least one of the following: blue-white veil, multiple brown dots, pseudopods, radial streaming, scarlike depigmentation, peripheral block dots/globules, 5 to 6 colors, multiple blue/gray dots, and a broadened network.¹² 

Again, the lesion shown in Figure 2 meets the criteria concerning for melanoma based on this algorithm.

The ABCD rule is a more technical dermoscopic evaluation algorithm developed in 1994 by Stolz and colleagues. This method yields a numeric value called the total dermoscopic score (TDS) based on Asymmetry, Border pigment pattern, Color variation, and 5 Different structural components. 

The assessment of asymmetry is determined by analyzing the lesion in a plane bisected by 2 axes set at 90°. A score from 0 to 2 is assigned based on the number of axes showing asymmetry in shape, color, or structure. Border pigment pattern is analyzed by dividing the lesion into eighths. A sharp, abrupt change in pigment pattern at the periphery earns the lesion 1 point for each division. The determination of the color variation score is done by adding 1 point for each white, red, light brown, dark brown, blue-gray, or black region identified in the lesion. Last, the lesion is assigned 1 point for each of 5 different structural components observed in the lesion, which include networks, homogenous areas, dots, globules, and streaks. To be significant, homogenous areas must be at least 10% of the lesion, and multiple branched streaks or dots must be visible. The TDS is calculated with the following formula: TDS = 1.3 x Asymmetry + 0.1 x Border + 0.5 x Color + 0.5 x Different. Higher scores are more concerning of melanoma, with scores > 5.45 suggesting melanoma.¹³ The lesion shown in Figure 2 registers a 7.7 by this metric.

Henning and colleagues developed the CASH algorithm in 2006 with the intention of assisting less experienced dermoscopy users with lesion evaluation.¹⁴ This algorithm tallies points for Color, Architectural disorder, Symmetry, and Homogeneity. One point is attributed to a lesion for each light brown, dark brown, black, red, white, and/or blue region present. Architectural disorder is assigned a point value between 0 and 2, with 0 indicating the absence of or minimal architectural disorder, 1 indicating moderate disorder, and 2 indicating marked disorder. Symmetry is assigned a point value between 0 and 2, with 0 points assigned to a lesion that exhibits biaxial symmetry, 1 point assigned to a lesion that exhibits monoaxial symmetry, and 2 points assigned to a lesion that exhibits biaxial asymmetry. Finally, 1 point is attributed to a lesion for evidence of each of the following: atypical network, dots/globules, streaks/pseudopods, blue-white veil, regression structures, blotches > 10% of the overall lesion size, and polymorphous blood vessels. The lesion in Figure 2 scores 16 points out of the maximum total CASH score of 17. Any lesion scoring 8 or more is suggestive of malignant melanoma.¹⁴

Finally, the TADA method was developed by Rogers and colleagues in 2016.¹⁵ This method uses sequential questions to evaluate lesions. First, “Does the lesion exhibit clear dermoscopic evidence of an angioma, dermatofibroma, or seborrheic keratosis?” If “yes,” then no additional dermoscopic evaluation is necessary, and it is recommended to monitor the lesion. If the answer to the first question is “no,” then ask, “Does the lesion exhibit architectural disorder?” The presence of architectural disorder is based on an overall impression of the lesion, which includes symmetry with regard to structures and colors. Any lesion deemed to exhibit architectural disorder should be biopsied. If the lesion has no architectural disorder, the third question is, “Does the lesion contain any starburst patterns, blue-black or gray coloration, shiny white structures, negative networks, ulcers or erosions, and/or vessels?” If “yes,” then the lesion should be biopsied. Since the lesion in Figure 2 exhibits marked architectural disorder in terms of symmetry and color, analysis of the lesion with the TADA method would yield a recommendation for biopsy.¹⁵

 

Dermoscopy in Practice

A. Bernard Ackerman, MD, a key figure in the modern era of dermatopathology, wrote an editorial for the Journal of the American Academy of Dermatology in 1985 titled “No one should die of malignant melanoma.” The editorial highlighted that the visual changes associated with melanoma often manifest years prior to malignant invasion and advocated that all physicians should have competence in melanoma detection, specifically mentioning the importance of training primary care physicians (PCPs), dermatologists, and pathologists in this regard.¹⁶ This sentiment is equally as true now as it was in 1985.

Naked eye examination paired with an evaluation of patient risk factors for melanoma, including fair skin types, significant sun exposure history, history of sunburn, geographic location, and personal and family history of melanoma, are the foundation of melanoma detection efforts.¹⁷ Studies suggest that the triage skills of PCPs could be improved in regard to the evaluation of pigmented lesions. Primary care residents, for instance, did not accurately diagnose 40% of malignant melanoma cases.^18,19 Additionally, a meta-analysis demonstrated that PCP accuracy when diagnosing malignant melanoma ranged between 49% and 80%, significantly less than the 85% to 89% exhibited by practicing dermatologists.¹⁹ Dermoscopy could be incorporated as an element of the skin examination to enhance lesion discrimination among PCPs, as it has proven use in dermatologic practice.

Dermoscopy is not readily used by PCPs. A survey study of 705 family practitioners in the US performed by Morris and colleagues demonstrated that only 8.3% of participants currently use a dermatoscope to evaluate pigmented lesions.²⁰ The most common barriers to dermoscopy use cited by PCPs in the US include the cost of the dermatoscope, the time required to acquire proficiency, and the lack of financial reimbursement.¹⁶ True utilization of dermoscopy among PCPs is higher than this figure suggests due to the number of PCPs who access dermoscopic evaluations via teledermatology. All 21 Veterans Integrated Services Networks of the Veterans Health Administration (VHA) system, for instance, participate in teledermatology and jointly employ more than 1,150 trained telehealth clinical technicians who created a collective 107,000 teledermatology encounters with and without dermoscopy for evaluation by dermatologists in the most recent fiscal year(Martin Weinstock, written communication, October 2017). Nonetheless, it is necessary to determine the contribution that wider utilization of dermoscopy among PCPs would have on melanoma surveillance.

Studies show that dermoscopic algorithms improve the sensitivity while slightly decreasing the specificity of PCPs to detect melanoma compared with that of the naked eye examination. Dolianitis and colleagues demonstrated that a baseline sensitivity of 60.9% for melanoma detection improved to 85.4% with the 7-point checklist, 85.4% with Menzies method, and 77.5% with the ABCD rule, while the baseline specificity of 85.4% moderated to 73.0%, 77.7%, and 80.4%, respectively, among 61 medical practitioners after studying dermoscopy techniques from 2 CDs.²¹ Westerhoff and colleagues performed a randomized controlled trial with 74 PCPs to determine the effect of a minimal intervention on melanoma diagnostic accuracy. The intervention consisted of providing participants in the experimental group with an atlas of microscopic features common to melanoma to be read at the participants’ leisure, a 1-hour presentation on microscopy, and a 25-questionpractice quiz. Participants randomized to the intervention group improved their diagnostic accuracy from 57.8% to 75.9% with the use of dermoscopy. This group also experiencedimproved accuracy in its clinical diagnosis of melanoma from 54.6% to 62.7%.²²

Argenziano and colleagues demonstrated similar results after PCPs attended a 4-hour workshop on dermoscopy. The 73 PCPs in this study evaluated 2,522 lesions randomized to naked eye examination or dermoscopy. The BMR, calculated from the data provided, improved from 12.6:1 to 10.5:1, respectively, when dermoscopy was incorporated into lesion analysis, while the sensitivity increased from 54.1% to 79.2% and the negative predictive value increased from 95.8% to 98.1%. It is important to note that the BMR and negative predictive value improved in tandem, indicating that PCPs were more discriminatory with their referrals for evaluation by dermatology while capturing a greater percentage of melanomas.²³

These studies are not without limitations that preclude broad generalizations. For example, Dolianitis and colleagues and Westerhoff and colleagues provided participants with dermoscopic images of the lesions to be evaluated instead of requiring personal use of a dermatoscope, whereas the study by Argenziano and colleagues incorporated only 6 histopathologically proven malignant melanomas into each of the naked eye examination and the experimental dermoscopy groups.^21-23 Yet these studies suggest that broader use of dermoscopy among PCPs could improve the accuracy of melanoma detection given clinically relevant training.

Several additional studies identify positive correlations associated with dermoscopy use among PCPs. A recent survey of 425 French general practitioners found that 8% of the study participants acknowledged owning a dermatoscope. Dermatoscope owners spent a statistically significant longer time analyzing each pigmented skin lesions, exhibited greater confidence in their analysis of pigmented lesions, and issued fewer overall referrals to dermatologists.²⁴

Similarly, Rosendahl and colleagues evaluated the number needed to treat (NNT) (equivalent to the BMR) among 193 Australian PCPs and found that the NNT was inversely correlated to the frequency with which the physicians used dermoscopy. However, it was difficult to determine the definitive cause of the reduced NNT in this study because a similar effect was observed when NNT was evaluated based on general practitioner subspecialization.²⁵ Again, despite limitations, these studies suggest that increased dermoscopy use among PCPs could reduce the morbidity of lifelong scarring as well as the short-term anxiety associated with a possible melanoma diagnosis.

 

Limitations

Even in the hands of a trained dermatologist, dermoscopy has limitations. Featureless melanoma is a term applied to melanoma lesions bereft of classical findings on both naked eye examination and dermoscopy. Menzies, a dermatologic pioneer in dermoscopy, acknowledged this limitation in 1996 while showing that 8% of melanomas evaded dermoscopic detection. He proceeded to discuss the importance of clinical history in melanoma detection because all of the featureless melanomas exhibited recent changes in size, shape, and/or color.²⁶ More recently, sequential dermoscopy (mole mapping) imaging has been implemented to successfully identify these lesions.²⁷ Thus, dermoscopy cannot replace dermatologists trained in the art of visual assessment with honed clinical diagnostic acumen. Rather, dermoscopy is a tool to enhance the assessment of clinically suspicious lesions and aid diagnostic discrimination of uncertain pigmented lesions.

Conclusion

Primary care physicians are on the frontline of medicine and often the first to have the opportunity to detect the presence of melanoma. Notably, 52.2% of the 884.7 million medical office visits performed annually in the US are with PCPs.²⁸ Despite the benefits, dermoscopy is not uniformly used by dermatologists in the US. Of dermatologists practicing for more than 20 years, 76.2% use dermoscopy compared with 97.8% of dermatologists in practice for less than 5 years. This illustrates an increased use in tandem with dermatology residencies integrating dermoscopy training as a component of the curriculum, showing the importance of incorporating dermoscopy into medical school and residency training for PCPs.^.29-31 Guidelines regarding dermoscopy training and dermoscopic evaluation algorithms should be established, routinely taught in medical education, and actively incorporated into training curriculum for PCPs in order to improve patient care and realize the potential health care savings associated with the early diagnosis and treatment of melanoma. Dermoscopic-teledermatology consultations present a viable opportunity within the VHA to expedite access to care for veterans and simultaneously offer evaluative feedback on lesions to referring PCPs, as skilled, dermoscopy-trained dermatologists render the diagnoses. Given the devastating mortality rate of melanoma, continued multidisciplinary education on identifying melanoma is of the utmost importance for patient care. Widespread implementation of dermoscopy and dermoscopic-teledermatology consultations could save lives and slow the ever-increasing economic burden associated with melanoma treatment, costing $1.467 billion in 2016.³²

From 1982 to 2011, the melanoma incidence rate doubled in the US.¹ In 2018, an estimated 87,290 cases of melanoma in situ and 91,270 cases of invasive melanoma will be diagnosed in the US, and 9,320 deaths will be attributable to melanoma.² Early detection of melanoma is critically important to reduce melanoma-related mortality, with 5-year survival rates as high as 97% at stage 1A vs a 20% 5-year survival when there is distant metastasis.^2,3 Melanoma is particularly relevant for medical providers working with veterans because melanoma disproportionately affects service members with an incidence rate ratio of 1.62 (95% confidence interval [CI], 1.40-1.86) compared with that of the general population.⁴

Biopsy is the definitive diagnostic tool for melanoma. Histologic analysis differentiates melanoma from seborrheic keratoses, pigmented nevi, dermatofibromas, and other pigmented lesions that can resemble melanoma on clinical examination. However, biopsy must be used judiciously as unnecessary biopsies contribute to health care costs and leave scars, which can have psychosocial implications. With benign nevi outnumbering melanoma about 2 million to 1, biopsy is indicated once a threshold of suspicion is obtained.⁵

Dermoscopic Tool

Dermoscopy is a microscopy-based tool to improve noninvasive diagnostic discrimination of skin lesions based on color and structure analysis. Coloration provides an indication of the composition of elements present in the skin with keratin appearing yellow, blood appearing red, and collagen appearing white. Coloration also suggests pigment depth as melanin appears black when located in the stratum corneum, brown when located deeper in the epidermis, and blue when located in the dermis.⁶ Finally, characteristic histopathologic alterations in the dermoepidermal junction, rete ridges, pigment-containing cells, and/or melanocyte granules that occur in melanoma are recognizable with dermoscopy.⁶

In 2001, Bafounta and colleagues performed the first meta-analysis on the efficacy of dermoscopy compared with that of clinical evaluation, finding that dermoscopy performed specifically by dermatology-trained clinicians improved the accuracy of identifying melanoma from an odds ratio of 16 (95% CI, 9-31) with naked eye examination to 76 (95% CI, 25-223) with dermoscopy.⁷

More recently, Terushkin and colleagues demonstrated that diagnosis specificity improves when a general dermatologist is trained in dermoscopic pattern recognition. Naked eye examination produced a benign to malignant ratio (BMR) of 18.4:1, indicating that about 18 of 19 biopsies considered suspicious for melanoma ultimately yielded benign melanocytic lesions. Although the BMR for the general dermatologist experienced an increase after dermoscopy training, the ratio eventually decreased to 7.9:1.⁸

Dermoscopic Analysis

Some of the common patterns recognized in melanoma are demonstrated in Figures 1 and 2. Figure 1 is a close-up of a patient’s upper back showing a solitary asymmetric melanocytic lesion containing multiple red, brown, black, and blue hues. 

The lesion is highly suspicious for melanoma. Key patterns identified under dermoscopy in Figure 2 increase the level of suspicion. The pink circle in the upper left of the lesion demonstrates a scarlike regression of pigment structure. 

The orange triangle signifies a region with marked variability in color called an atypical pigment network, and the centrally located yellow circle and gray square identify interspersed atypical dots and globules of color. The red rectangle on the right highlights irregular streaking, linear radial projections suggestive of superficial spreading melanoma. The green line identifies hypopigmentation with surrounding curvilinear globular structures collectively known as a negative network.  Finally, the bottom blue triangle overlies an area with a hazy blue tinge called a blue-white veil, indicating the presence of melanocytes deep in the dermis (Table 1).^6,9

Pattern analysis, the dermoscopic interpretation method preferred by pigmented lesion specialists, requires simultaneously assessing numerous lesion patterns that vary depending on body site.¹⁰ Alternative dermoscopic algorithms that focus on the most common features of melanoma have been developed to aid practitioners with the interpretation of dermoscopy findings: the 7-point checklist, the Menzies method, the ABCD rule, and the CASH algorithm (Tables 2, 3, 4, and 5). 

To apply these algorithms to evaluate the lesion in Figures 1 and 2 ( eAppendix

Document

fp-35-5s-s39_eappendix.pdf

Media Folder

Media Root

).^11-14 The triage amalgamated dermoscopic algorithm (TADA) method, a newer algorithm designed for novice dermoscopy users, is also discussed briefly.

Argenziano and colleagues developed the 7-point checklist in 1998. Two points are assigned to the lesion for each of the major criteria and 1 point for each minor criteria. 

The major criteria include an atypical pigment network, blue-white veil, and atypical vascular pattern; the minor criteria include irregular streaks, irregular pigmentation, irregular dots/globules, and regression structures.¹¹ The lesion shown in Figure 2 scores an 8 out of 10 by this metric, handily surpassing the 3 points required to suggest melanoma.¹¹

The Menzies method was developed by Menzies and colleagues in 1996. To be classified as melanoma, the pigmented lesion must show an absence of pattern symmetry and color uniformity while simultaneously exhibiting at least one of the following: blue-white veil, multiple brown dots, pseudopods, radial streaming, scarlike depigmentation, peripheral block dots/globules, 5 to 6 colors, multiple blue/gray dots, and a broadened network.¹² 

Again, the lesion shown in Figure 2 meets the criteria concerning for melanoma based on this algorithm.

The ABCD rule is a more technical dermoscopic evaluation algorithm developed in 1994 by Stolz and colleagues. This method yields a numeric value called the total dermoscopic score (TDS) based on Asymmetry, Border pigment pattern, Color variation, and 5 Different structural components. 

The assessment of asymmetry is determined by analyzing the lesion in a plane bisected by 2 axes set at 90°. A score from 0 to 2 is assigned based on the number of axes showing asymmetry in shape, color, or structure. Border pigment pattern is analyzed by dividing the lesion into eighths. A sharp, abrupt change in pigment pattern at the periphery earns the lesion 1 point for each division. The determination of the color variation score is done by adding 1 point for each white, red, light brown, dark brown, blue-gray, or black region identified in the lesion. Last, the lesion is assigned 1 point for each of 5 different structural components observed in the lesion, which include networks, homogenous areas, dots, globules, and streaks. To be significant, homogenous areas must be at least 10% of the lesion, and multiple branched streaks or dots must be visible. The TDS is calculated with the following formula: TDS = 1.3 x Asymmetry + 0.1 x Border + 0.5 x Color + 0.5 x Different. Higher scores are more concerning of melanoma, with scores > 5.45 suggesting melanoma.¹³ The lesion shown in Figure 2 registers a 7.7 by this metric.

Henning and colleagues developed the CASH algorithm in 2006 with the intention of assisting less experienced dermoscopy users with lesion evaluation.¹⁴ This algorithm tallies points for Color, Architectural disorder, Symmetry, and Homogeneity. One point is attributed to a lesion for each light brown, dark brown, black, red, white, and/or blue region present. Architectural disorder is assigned a point value between 0 and 2, with 0 indicating the absence of or minimal architectural disorder, 1 indicating moderate disorder, and 2 indicating marked disorder. Symmetry is assigned a point value between 0 and 2, with 0 points assigned to a lesion that exhibits biaxial symmetry, 1 point assigned to a lesion that exhibits monoaxial symmetry, and 2 points assigned to a lesion that exhibits biaxial asymmetry. Finally, 1 point is attributed to a lesion for evidence of each of the following: atypical network, dots/globules, streaks/pseudopods, blue-white veil, regression structures, blotches > 10% of the overall lesion size, and polymorphous blood vessels. The lesion in Figure 2 scores 16 points out of the maximum total CASH score of 17. Any lesion scoring 8 or more is suggestive of malignant melanoma.¹⁴

Finally, the TADA method was developed by Rogers and colleagues in 2016.¹⁵ This method uses sequential questions to evaluate lesions. First, “Does the lesion exhibit clear dermoscopic evidence of an angioma, dermatofibroma, or seborrheic keratosis?” If “yes,” then no additional dermoscopic evaluation is necessary, and it is recommended to monitor the lesion. If the answer to the first question is “no,” then ask, “Does the lesion exhibit architectural disorder?” The presence of architectural disorder is based on an overall impression of the lesion, which includes symmetry with regard to structures and colors. Any lesion deemed to exhibit architectural disorder should be biopsied. If the lesion has no architectural disorder, the third question is, “Does the lesion contain any starburst patterns, blue-black or gray coloration, shiny white structures, negative networks, ulcers or erosions, and/or vessels?” If “yes,” then the lesion should be biopsied. Since the lesion in Figure 2 exhibits marked architectural disorder in terms of symmetry and color, analysis of the lesion with the TADA method would yield a recommendation for biopsy.¹⁵

 

Dermoscopy in Practice

A. Bernard Ackerman, MD, a key figure in the modern era of dermatopathology, wrote an editorial for the Journal of the American Academy of Dermatology in 1985 titled “No one should die of malignant melanoma.” The editorial highlighted that the visual changes associated with melanoma often manifest years prior to malignant invasion and advocated that all physicians should have competence in melanoma detection, specifically mentioning the importance of training primary care physicians (PCPs), dermatologists, and pathologists in this regard.¹⁶ This sentiment is equally as true now as it was in 1985.

Naked eye examination paired with an evaluation of patient risk factors for melanoma, including fair skin types, significant sun exposure history, history of sunburn, geographic location, and personal and family history of melanoma, are the foundation of melanoma detection efforts.¹⁷ Studies suggest that the triage skills of PCPs could be improved in regard to the evaluation of pigmented lesions. Primary care residents, for instance, did not accurately diagnose 40% of malignant melanoma cases.^18,19 Additionally, a meta-analysis demonstrated that PCP accuracy when diagnosing malignant melanoma ranged between 49% and 80%, significantly less than the 85% to 89% exhibited by practicing dermatologists.¹⁹ Dermoscopy could be incorporated as an element of the skin examination to enhance lesion discrimination among PCPs, as it has proven use in dermatologic practice.

Dermoscopy is not readily used by PCPs. A survey study of 705 family practitioners in the US performed by Morris and colleagues demonstrated that only 8.3% of participants currently use a dermatoscope to evaluate pigmented lesions.²⁰ The most common barriers to dermoscopy use cited by PCPs in the US include the cost of the dermatoscope, the time required to acquire proficiency, and the lack of financial reimbursement.¹⁶ True utilization of dermoscopy among PCPs is higher than this figure suggests due to the number of PCPs who access dermoscopic evaluations via teledermatology. All 21 Veterans Integrated Services Networks of the Veterans Health Administration (VHA) system, for instance, participate in teledermatology and jointly employ more than 1,150 trained telehealth clinical technicians who created a collective 107,000 teledermatology encounters with and without dermoscopy for evaluation by dermatologists in the most recent fiscal year(Martin Weinstock, written communication, October 2017). Nonetheless, it is necessary to determine the contribution that wider utilization of dermoscopy among PCPs would have on melanoma surveillance.

Studies show that dermoscopic algorithms improve the sensitivity while slightly decreasing the specificity of PCPs to detect melanoma compared with that of the naked eye examination. Dolianitis and colleagues demonstrated that a baseline sensitivity of 60.9% for melanoma detection improved to 85.4% with the 7-point checklist, 85.4% with Menzies method, and 77.5% with the ABCD rule, while the baseline specificity of 85.4% moderated to 73.0%, 77.7%, and 80.4%, respectively, among 61 medical practitioners after studying dermoscopy techniques from 2 CDs.²¹ Westerhoff and colleagues performed a randomized controlled trial with 74 PCPs to determine the effect of a minimal intervention on melanoma diagnostic accuracy. The intervention consisted of providing participants in the experimental group with an atlas of microscopic features common to melanoma to be read at the participants’ leisure, a 1-hour presentation on microscopy, and a 25-questionpractice quiz. Participants randomized to the intervention group improved their diagnostic accuracy from 57.8% to 75.9% with the use of dermoscopy. This group also experiencedimproved accuracy in its clinical diagnosis of melanoma from 54.6% to 62.7%.²²

Argenziano and colleagues demonstrated similar results after PCPs attended a 4-hour workshop on dermoscopy. The 73 PCPs in this study evaluated 2,522 lesions randomized to naked eye examination or dermoscopy. The BMR, calculated from the data provided, improved from 12.6:1 to 10.5:1, respectively, when dermoscopy was incorporated into lesion analysis, while the sensitivity increased from 54.1% to 79.2% and the negative predictive value increased from 95.8% to 98.1%. It is important to note that the BMR and negative predictive value improved in tandem, indicating that PCPs were more discriminatory with their referrals for evaluation by dermatology while capturing a greater percentage of melanomas.²³

These studies are not without limitations that preclude broad generalizations. For example, Dolianitis and colleagues and Westerhoff and colleagues provided participants with dermoscopic images of the lesions to be evaluated instead of requiring personal use of a dermatoscope, whereas the study by Argenziano and colleagues incorporated only 6 histopathologically proven malignant melanomas into each of the naked eye examination and the experimental dermoscopy groups.^21-23 Yet these studies suggest that broader use of dermoscopy among PCPs could improve the accuracy of melanoma detection given clinically relevant training.

Several additional studies identify positive correlations associated with dermoscopy use among PCPs. A recent survey of 425 French general practitioners found that 8% of the study participants acknowledged owning a dermatoscope. Dermatoscope owners spent a statistically significant longer time analyzing each pigmented skin lesions, exhibited greater confidence in their analysis of pigmented lesions, and issued fewer overall referrals to dermatologists.²⁴

Similarly, Rosendahl and colleagues evaluated the number needed to treat (NNT) (equivalent to the BMR) among 193 Australian PCPs and found that the NNT was inversely correlated to the frequency with which the physicians used dermoscopy. However, it was difficult to determine the definitive cause of the reduced NNT in this study because a similar effect was observed when NNT was evaluated based on general practitioner subspecialization.²⁵ Again, despite limitations, these studies suggest that increased dermoscopy use among PCPs could reduce the morbidity of lifelong scarring as well as the short-term anxiety associated with a possible melanoma diagnosis.

 

Limitations

Even in the hands of a trained dermatologist, dermoscopy has limitations. Featureless melanoma is a term applied to melanoma lesions bereft of classical findings on both naked eye examination and dermoscopy. Menzies, a dermatologic pioneer in dermoscopy, acknowledged this limitation in 1996 while showing that 8% of melanomas evaded dermoscopic detection. He proceeded to discuss the importance of clinical history in melanoma detection because all of the featureless melanomas exhibited recent changes in size, shape, and/or color.²⁶ More recently, sequential dermoscopy (mole mapping) imaging has been implemented to successfully identify these lesions.²⁷ Thus, dermoscopy cannot replace dermatologists trained in the art of visual assessment with honed clinical diagnostic acumen. Rather, dermoscopy is a tool to enhance the assessment of clinically suspicious lesions and aid diagnostic discrimination of uncertain pigmented lesions.

Conclusion

Primary care physicians are on the frontline of medicine and often the first to have the opportunity to detect the presence of melanoma. Notably, 52.2% of the 884.7 million medical office visits performed annually in the US are with PCPs.²⁸ Despite the benefits, dermoscopy is not uniformly used by dermatologists in the US. Of dermatologists practicing for more than 20 years, 76.2% use dermoscopy compared with 97.8% of dermatologists in practice for less than 5 years. This illustrates an increased use in tandem with dermatology residencies integrating dermoscopy training as a component of the curriculum, showing the importance of incorporating dermoscopy into medical school and residency training for PCPs.^.29-31 Guidelines regarding dermoscopy training and dermoscopic evaluation algorithms should be established, routinely taught in medical education, and actively incorporated into training curriculum for PCPs in order to improve patient care and realize the potential health care savings associated with the early diagnosis and treatment of melanoma. Dermoscopic-teledermatology consultations present a viable opportunity within the VHA to expedite access to care for veterans and simultaneously offer evaluative feedback on lesions to referring PCPs, as skilled, dermoscopy-trained dermatologists render the diagnoses. Given the devastating mortality rate of melanoma, continued multidisciplinary education on identifying melanoma is of the utmost importance for patient care. Widespread implementation of dermoscopy and dermoscopic-teledermatology consultations could save lives and slow the ever-increasing economic burden associated with melanoma treatment, costing $1.467 billion in 2016.³²