Measuring quality and value in genetic counseling: The current landscape and future directions.

Genetic counselors strive to provide high-quality genetic services. To do so, it is essential to define quality in genetic counseling and identify opportunities for improvement. This Professional Issues article provides an overview of the evaluation of healthcare quality in genetic counseling. The National Society of Genetic Counselors' Research, Quality, and Outcomes Committee partnered with Discern Health, a value-based healthcare policy consulting firm, to develop a care continuum model of genetic counseling. Using the proposed model, currently available quality measures relevant to genetic counseling in the US healthcare system were assessed, allowing for the identification of gaps and priority areas for further development. A total of 560 quality measures were identified that can be applied to various aspects of the care continuum model across a range of clinical specialty areas in genetic counseling, although few measures were specific to genetic counseling or genetic conditions. Areas where quality measures were lacking included: attitudes toward genetic testing, family communication, stigma, and issues of justice, equity, diversity, and inclusion. We discuss these findings and other strategies for an evidence-based approach to quality in genetic counseling. Strategic directions for the genetic counseling profession should include a consolidated approach to research on quality and value of genetic counseling, development of quality metrics and patient-experience measures, and engagement with other improvement activities. These strategies will allow for benchmarking, performance improvement, and future implementation in accountability programs which will strengthen genetic counseling as a profession that provides evidence-based high-quality care to all patients.

Improving Oncology Quality Measurement in Accountable Care: Filling Gaps with Cross-Cutting Measures.

Payment for health care services, including oncology services, is shifting from volume-based fee-for-service to value-based accountable care. The objective of accountable care is to support providers with flexibility and resources to reform care delivery, accompanied by accountability for maintaining or improving outcomes while lowering costs. These changes depend on health care payers, systems, physicians, and patients having meaningful measures to assess care delivery and outcomes and to balance financial incentives for lowering costs while providing greater value. Gaps in accountable care measure sets may cause missed signals of problems in care and missed opportunities for improvement. Measures to balance financial incentives may be particularly important for oncology, where high cost and increasingly targeted diagnostics and therapeutics intersect with the highly complex and heterogeneous needs and preferences of cancer patients. Moreover, the concept of value in cancer care, defined as the measure of outcomes achieved per costs incurred, is rarely incorporated into performance measurement. This article analyzes gaps in oncology measures in accountable care, discusses challenging measurement issues, and offers strategies for improving oncology measurement. Discern Health analyzed gaps in accountable care measure sets for 10 cancer conditions that were selected based on incidence and prevalence; impact on cost and mortality; a diverse range of high-cost diagnostic procedures and treatment modalities (e.g., genomic tumor testing, molecularly targeted therapies, and stereotactic radiotherapy); and disparities or performance gaps in patient care. We identified gaps by comparing accountable care set measures with high-priority measurement opportunities derived from practice guidelines developed by the National Comprehensive Cancer Network and other oncology specialty societies. We found significant gaps in accountable care measure sets across all 10 conditions. For each gap, we searched for available measures not already being used in programs. Where existing measures did not cover gaps, we recommended refinements to existing measures or proposed measures for development. We shared the results of the measure gap analysis with a roundtable of national experts in cancer care and oncology measurement. During a web meeting and an in-person meeting, the roundtable reviewed the gap analysis and identified priority opportunities for improving measurement. The group determined that overreliance on condition-specific process measures is problematic because of rapidly changing evidence and increasing personalization of cancer care. The group's primary recommendation for enhancing measure sets was to prioritize and develop effective cross-cutting measures that assess clinical and patient-reported outcomes, including shared decision making, care planning, and symptom control. The group also prioritized certain safety and structural measures to complement condition-specific process measures. Further, the group explored strategies for using clinical pathways and devising layered measurement approaches to improve measurement for accountable care. This article presents the roundtable's conclusions and recommendations for next steps. DISCLOSURES: Funding for this project was provided by the National Pharmaceutical Council (NPC). Westrich and Dubois are employees of the NPC. Valuck is a partner with Discern Health. Blaisdell and Dugan are employed by Discern Health. McClellan reports fees for serving on the Johnson & Johnson Board of Directors. Dugan reports consulting fees from the National Committee for Quality Assurance and Pharmacy Quality Alliance. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. Study concept and design were contributed by Blaisdell, Valuck, Dugan, and Westrich. Blaisdell took the lead in data collection, along with Valuck and Dugan, and data interpretation was performed by Valuck, Blaisdell, Westrich, and Dubois. The manuscript was written by Blaisdell, along with Valuck and Dugan, and revised by Valuck, Westrich, Miller, and McClellan.

Solutions for filling gaps in accountable care measure sets.

OBJECTIVES: A primary objective of accountable care is to support providers in reforming care to improve outcomes and lower costs. Gaps in accountable care measure sets may cause missed opportunities for improvement and missed signals of problems in care. Measures to balance financial incentives may be particularly important for high-cost conditions or specialty treatments. This study explored gaps in measure sets for specific conditions and offers strategies for more comprehensive measurement that do not necessarily require more measures. STUDY DESIGN: A descriptive analysis of measure gaps in accountable care programs and proposed solutions for filling the gaps. METHODS: We analyzed gaps in 2 accountable care organization measure sets for 20 high-priority clinical conditions by comparing the measures in those sets with clinical guidelines and assessing the use of outcome measures. Where we identified gaps, we looked for existing measures to address the gaps. Gaps not addressed by existing measures were considered areas for measure development or measurement strategy refinement. RESULTS: We found measure gaps across all 20 conditions, including those conditions that are commonly addressed in current measure sets. In addition, we found many gaps that could not be filled by existing measures. Results across all 20 conditions informed recommendations for measure set improvement. CONCLUSIONS: Addressing all gaps in accountable care measure sets with more of the same types of measures and approaches to measurement would require an impractical number of measures and would miss the opportunity to use better measures and innovative approaches. Strategies for effectively filling measure gaps include using preferred measure types such as cross-cutting, outcome, and patient-reported measures. Program implementers should also apply new approaches to measurement, including layered and modular models.

The relationship between organizational culture and practice systems in primary care.

The patient-centered medical home is an approach to comprehensive primary care relying on well-developed systems. Research has shown that for practices to meet patient-centered medical home requirements, care models may need to be redesigned. However, there is a dearth of information about what factors are important to achieve this goal. Self-report surveys from 293 staff across 42 practices in Minnesota showed variation in use of systems and dimensions of organizational culture. Organizational cultures that emphasize collegiality and quality but not autonomy were significantly related to the use of clinician reminders, clinical quality evaluation and improvement, and clinical information systems.

Development and testing of performance measures for pharmacy services.

OBJECTIVE: To report on the status of the pilot work of PQA, a pharmacy quality alliance, to develop and test performance metrics of pharmacy services for use in quality improvement, benchmarking, and pay-for-performance benchmarks. DESIGN: Observational cohort study. SETTING: Three health plans (commercial, Medicare and Medicaid) located in the northeastern United States and one nationwide prescription drug plan. PATIENTS: Pharmacies of health plans with membership ranging from approximately 3,330 to nearly 1.7 million members. INTERVENTION: Pharmaceutical claims data for prescriptions dispensed at community pharmacies were analyzed. MAIN OUTCOME MEASURES: Not applicable. RESULTS: The four plans had pharmacy networks ranging from 653 to 53,153 pharmacies. When using a minimum sample of 30 members per measure, less than 10% of the pharmacies within the plans' networks were evaluable for all measures except the measure of high-risk drugs in the elderly. The measure for high-risk drugs in the elderly had 6,210 evaluable pharmacies in a network of 53,153. The measures for high-risk drugs in the elderly and medication adherence appear to have the greatest potential for use as performance measures in that they show room for improvement and variation among pharmacies. CONCLUSION: The ideal performance measure is relevant, scientifically sound, and feasible. Several of the measures that underwent testing possessed some, if not all, of the properties of an ideal performance measure. Strategies for aggregating data across health and drug plans may be useful for overcoming sample size challenges.

Availability of data for measuring physician quality performance.

OBJECTIVE: To evaluate measurement of physician quality performance, which is increasingly used by health plans as the basis of quality improvement, network design, and financial incentives, despite concerns about data and methodological challenges. STUDY DESIGN: Evaluation of health plan administrative claims and enrollment data. METHODS: Using administrative data from 9 health plans, we analyzed results for 27 well-accepted quality measures and evaluated how many quality events (patients eligible for a measure) were available per primary care physician and how different approaches for attributing patients to physicians affect the number of quality events per physician. RESULTS: Fifty-seven percent of primary care physicians had at least 1 patient who was eligible for at least 1 of the selected quality measures. Most physicians had few quality events for any single measure. As an example, for a measure evaluating appropriate treatment for children with upper respiratory tract infections, physicians on average had 14 quality events when care was attributed to physicians if they saw the patient at least once in the measurement year. The mean number of quality events dropped to 9 when attribution required that the physician provide care in at least 50% of a patient's visits. Few physicians had more than 30 quality events for any given measure. CONCLUSIONS: Available administrative data for a single health plan may provide insufficient information for benchmarking performance for individual physicians. Efforts are needed to develop consensus on assigning measure accountability and to expand information available for each physician, including accessing electronic clinical data, exploring composite measures of performance, and aggregating data across public and private health plans.

Benchmarking physician performance: reliability of individual and composite measures.

OBJECTIVE: To examine the reliability of quality measures to assess physician performance, which are increasingly used as the basis for quality improvement efforts, contracting decisions, and financial incentives, despite concerns about the methodological challenges. STUDY DESIGN: Evaluation of health plan administrative claims and enrollment data. METHODS: The study used administrative data from 9 health plans representing more than 11 million patients. The number of quality events (patients eligible for a quality measure), mean performance, and reliability estimates were calculated for 27 quality measures. Composite scores for preventive, chronic, acute, and overall care were calculated as the weighted mean of the standardized scores. Reliability was estimated by calculating the physician-to-physician variance divided by the sum of the physician-to-physician variance plus the measurement variance, and 0.70 was considered adequate. RESULTS: Ten quality measures had reliability estimates above 0.70 at a minimum of 50 quality events. For other quality measures, reliability was low even when physicians had 50 quality events. The largest proportion of physicians who could be reliably evaluated on a single quality measure was 8% for colorectal cancer screening and 2% for nephropathy screening among patients with diabetes mellitus. More physicians could be reliably evaluated using composite scores (<17% for preventive care, >7% for chronic care, and 15%-20% for an overall composite). CONCLUSIONS: In typical health plan administrative data, most physicians do not have adequate numbers of quality events to support reliable quality measurement. The reliability of quality measures should be taken into account when quality information is used for public reporting and accountability. Efforts to improve data available for physician profiling are also needed.