Clinical Quality Measures Research Articles

Reprint of: Development and Evaluation of a Global Malnutrition Composite Score.

Reprint of: Development and Evaluation of a Global Malnutrition Composite Score.

Development and testing of data infrastructure in the American College of Emergency Physicians' Clinical Emergency Data Registry for opioid-related research.

ObjectivePrior research has identified gaps in the capacity of electronic health records (EHRs) to capture the intricacies of opioid‐related conditions. We sought to enhance the opioid data infrastructure within the American College of Emergency Physicians’ Clinical Emergency Data Registry (CEDR), the largest national emergency medicine registry, through data mapping, validity testing, and feasibility assessment.MethodsWe compared the CEDR data dictionary to opioid common data elements identified through prior environmental scans of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Validity and feasibility assessments of CEDR opioid‐related data were conducted through the following steps: (1) electronic extraction of CEDR data meeting criteria for an opioid‐related emergency care visit, (2) manual chart review assessing the quality of the extracted data, (3) completion of feasibility scorecards, and (4) qualitative interviews with physician reviewers and informatics personnel.ResultsWe identified several data gaps in the CEDR data dictionary when compared with prior environmental scans including urine drug testing, opioid medication, and social history data elements. Validity testing demonstrated correct or partially correct data for >90% of most extracted CEDR data elements. Factors affecting validity included lack of standardization, data incorrectness, and poor delimitation between emergency department (ED) versus hospital care. Feasibility testing highlighted low‐to‐moderate feasibility of date and social history data elements, significant EHR platform variation, and inconsistency in the extraction of common national data standards (eg, Logical Observation Identifiers Names and Codes, International Classification of Diseases, Tenth Revision codes).ConclusionsWe found that high‐priority data elements needed for opioid‐related research and clinical quality measurement, such as demographics, medications, and diagnoses, are both valid and can be feasibly captured in a national clinical quality registry. Future work should focus on implementing structured data collection tools, such as standardized documentation templates and adhering to data standards within the EHR that would better characterize ED‐specific care for opioid use disorder and related research.

Consensus Quality Measures and Dose Constraints for Breast Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel

Using evidence-based radiation therapy to direct care for patients with breast cancer is critical to standardize practice, improve safety, and optimize outcomes. To address this need, the Veterans Affairs (VA) National Radiation Oncology Program (NROP) established the VA Radiation Oncology Quality Surveillance Program to develop clinical quality measures (QMs). The VA NROP contracted with the American Society for Radiation Oncology to commission 5 Blue Ribbon Panels for breast, lung, prostate, rectal, and head and neck cancers. The Breast Cancer Blue Ribbon Panel experts worked collaboratively with the NROP to develop consensus QMs for use throughout the VA system, establishing a set of QMs for patients in several areas, including consultation and work-up; simulation, treatment planning, and treatment; and follow-up care. As part of this initiative, consensus dose-volume histogram (DVH) constraints were outlined. In total, 36 QMs were established. Herein, we review the process used to develop QMs and final consensus QMs pertaining to all aspects of radiation patient care, as well as DVH constraints. The QMs and expert consensus DVH constraints are intended for ongoing quality surveillance within the VA system and centers providing community care for Veterans. They are also available for use by greater non-VA community measures of quality care for patients with breast cancer receiving radiation.

Don't Let Perfect Be the Enemy of Good: A Proof of Concept for a Custom National Data Repository of Quality Measures for Free and Charitable Clinics.

Purpose:Free and charitable clinics (FCCs), nonprofits that utilize volunteer licensed health care professionals to provide health services at no cost or a small fee to low-income uninsured patients who are disproportionately from underrepresented communities, have been part of the safety net for over a century. Approximately 1400 known FCCs serve two million patients annually. Despite their longevity and sizable number, evidence regarding the quality of care in FCCs is lacking. We report new evidence generated by a national initiative, the Roadmap to Health Equity. Established in 2017, this consortium is co-led by two national organizations serving FCCs and an academic institution. It has involved more than 150 FCC stakeholders with the shared goal of improving the quality of care and reducing inequities. The centerpiece is a custom national data repository of 15 validated clinical quality measures and patient-level characteristics.Methods:Fifty FCCs pilot tested the data repository. Clinics submitted patient-level data on two blood pressure (BP) measures and at least one additional measure. Descriptive statistics were stratified by sex, race, ethnicity, and language.Results:In 2021, 33 pilot FCCs from 21 states reported data across 13 of the 15 clinical measures, representing 34,359 unique patients. For example, on average, 60% of patients had controlled BP, but Black patients had lower rates of BP control than Hispanic and White patients (55.9% vs. 62.1% and 63.0%, respectively).Conclusion:Our findings demonstrate a proof of concept. By standardizing quality measures alongside patient characteristics, clinics can become aware of racial/ethnic inequalities in health outcomes. This information can motivate clinics to investigate the causes and implement solutions. In an environment where outcome data from FCCs are scarce, the new national data repository lays the foundation for routine stratified reporting of a range of quality outcomes for an important safety net for the uninsured.

Association of Medicare Advantage Premiums With Measures of Quality and Patient Experience

Quality of care varies substantially across Medicare Advantage plans. The price information that Medicare Advantage enrollees are most likely to consider when selecting a Medicare Advantage plan is the monthly premium. Enrollees may select plans to minimize premium or, alternatively, use premium as a proxy for quality and select plans with higher premiums; however, quality implications of these choices are unknown. To determine the extent to which the quality of care offered by Medicare Advantage plans varies within vs across premium levels. This was a retrospective cross-sectional study of the population enrolled in Medicare Advantage plans in 2016 to 2017 using clinical quality measures from the Healthcare Effectiveness Data and Information Set (HEDIS), patient experience measures from the Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys, and administrative data. Data were analyzed from March 2021 to March 2022. Medicare Advantage monthly premium. Ten publicly reported 2017 HEDIS measures and 5 publicly reported 2017 CAHPS measures linearly transformed to a 0 to 100 scale. The 168 968 Medicare Advantage CAHPS respondents were representative of the enrollee population (14% were <65 years old and eligible through disability; 24% ≥80 years old; sex and race/ethnicity data were not considered); 40% were in 591 plans with no monthly premiums and less than 6% were in 144 plans with monthly premiums of $120 or more. There were from 77 054 to 2 139 422 enrollees by HEDIS measure. Among all Medicare Advantage enrollees, 79% were in plans with either a $0 premium or a low monthly premium (≤$60); patient experience and clinical quality were generally similar in these 2 categories of plans. To a small extent, enrollees in moderately high ($60-$120) and high (≥$120) premium plans reported better patient experience (+1.4 [95% CI, 0.7-2.1] and 2.2 [95% CI, 1.5-2.9] points) and received better clinical care (1.4 [95% CI, 0.3-2.5] to 3.3 [5% CI, 2.1-4.5] percentage points on most measures than those with $0 and low-premium plans. Quality differences within each premium level category were substantial; the within-premium category plan-level SDs were 6.5 points and 7.2 percentage points for patient experience and clinical quality, respectively. A plan at the 50th percentile of clinical quality and patient experience in the high premium category would fall in the 65th and 62nd percentile within the $0-premium category, respectively. This population-based cross-sectional study found that although quality of care and patient experience were slightly higher with higher-premium plans, quality varied widely within each premium category. High-quality care and patient experience were found in each price category. Thus, paying higher premiums is not necessary for higher quality care in Medicare Advantage plans. Greater engagement of enrollees and advocates with quality of care and patient experience information for Medicare Advantage plan selection is recommended.

The effect of hospital discharge price increases on publicly reported measures of quality.

To determine if increases in hospital discharge prices are associated with improvements in clinical quality or patient experience. This study used Medicare cost report data and publicly available Medicare.gov Care Compare quality measures for approximately 3000 short-term care general hospitals between 2011 and 2018. We separately regressed quality measure scores on a lag of case mix adjusted discharge price, hospital fixed effects, and year indicators. Clinical quality measures included 30-day readmission rates for acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, hip and knee replacement, and pneumonia; risk-adjusted 30-day mortality rates for acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, and stroke; and 90-day complication rate for hip and knee replacement. Patient experience measures included the summary star rating and 10 domain measures reported through the Hospital Consumer Assessment of Healthcare Providers and Systems survey. We tested for heterogeneous effects by hospital ownership, number of beds, the commercial share of overall discharges, and market concentration. We linked hospitals identified in Medicare cost reports to Medicare.gov Care Compare quality measures. We excluded hospitals for which we could not identify a discharge price or that had an unrealistic price. There was no positive association between lagged discharge price and any clinical quality measure. For patient experience measures, a 2% increase in discharge price was not associated with overall patient satisfaction but was associated with small, statistically significant increases ranging from 0.01% to 0.02% (relative to mean scores) for seven of ten domain measures. There was a positive association for five of ten patient experience measures in competitive markets and one measure in both moderately concentrated and heavily concentrated markets. We found no evidence that hospitals use higher prices to make investments in clinical quality; patient experience improved, but only negligibly.

Diabetes Control in a Student-Run Free Clinic During the COVID-19 Pandemic.

Student run free health clinics (SRFCs) provide medical care to vulnerable populations in communities throughout the United States. The COVID-19 pandemic had a significant impact on the delivery of healthcare services and demanded a rapid adjustment in care delivery methods in both resource-rich and resource-poor settings. The aim of this study is to evaluate the impact of the pandemic on the management of chronic disease, specifically diabetes. Patients with diabetes who received care continuously throughout the pre-pandemic (face-to-face) and pandemic (telehealth) study periods at MedZou Community Health Center, a SRFC located in central Missouri, were evaluated. This sample of patients (n = 29) was evaluated on six quality measures including annual eye exams, blood pressure, hemoglobin A1c, chronic kidney disease monitoring, flu vaccination, and statin therapy. Overall diabetes care, as measured by the number of quality measures met per patient, decreased by 0.37 after the onset of the pandemic. The median COVID-era ranks were not statistically significantly different than the pre-pandemic ranks (z = 1.65, P = 0.099). Fewer patients received an influenza vaccination the year following the onset of the pandemic (10.3%) compared to the year before the pandemic (37.9%; difference in proportions 0.276, 95% CI 0.079, 0.473; p = 0.005). No other individual measures of diabetes care statistically differed significantly in the year after the pandemic began. Twenty-six (90%) patients received diabetes care using telehealth after the onset of the pandemic. Diabetes care using telehealth in a SRFC may be an acceptable alternative model when face-to-face visits are not feasible. Observed decreases in diabetes-related clinical quality measure performance warrant further study.

Comparing the impact of two contact isolation modes for hospitalised patients with Clostridioides difficile infection on the quality of care.

Clostridioides difficile causes healthcare-associated infections. Environmental spore acquisition is a major mode of transmission. Patient cohorting to prevent cross-transmission in healthcare-institutions is a reasonable component of an enhanced infection control strategy. The purpose of the study was to assess the effect of two different contact isolation modes on the quality of care of hospitalised patients with Clostridioides difficile infection (CDI). A retrospective cohort-study of patients with CDI hospitalised under one of two contact isolation modes: contact isolation in a multi-patient room without a dedicated nursing team vs. contact isolation in a permanent cohort isolation unit with a dedicated nursing team. Patients' files were reviewed for demographics, clinical characteristics, risk-assessment scores, clinical quality measures including the number of blood tests collected per day, the number of radiological tests applied per day and the time at which a radiological test was conducted, as process measurements, along with the length of stay and mortality, as outcome measures. The STROBE checklist for reporting observational studies was followed. One hundred and seventy-eight patients with CDI were included; 100 in a permanent cohort isolation unit and 78 under contact isolation in a multi-patient room. No difference was found in all clinical quality process measures and in all outcome measures. Multivariable logistic regression showed that nursing home residence was associated with in-hospital mortality (OR, 2.51; CI, 1.29-4.97; p= .007), whereas the mode of hospitalisation was not. The different contact isolation modes of hospitalisation did not compromise the quality of care of patients with CDI. Cohorting of patients with CDI is used to prevent cross-transmission, though it raises a major concern regarding quality of care. In this study we show there was no compromise in patient care, therefore it is a reasonable component of an enhanced infection control strategy in a hospital setting.

Consensus Quality Measures and Dose Constraints for Rectal Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology (ASTRO) Expert Panel

PurposeEnsuring high quality, evidence-based radiation therapy for patients with cancer is of the upmost importance. To address this need, the Veterans Affairs (VA) Radiation Oncology Program partnered with the American Society for Radiation Oncology and established the VA Radiation Oncology Quality Surveillance program. As part of this ongoing effort to provide the highest quality of care for patients with rectal cancer, a blue-ribbon panel comprised of rectal cancer experts was formed to develop clinical quality measures. Methods and MaterialsThe Rectal Cancer Blue Ribbon panel developed quality, surveillance, and aspirational measures for (a) initial consultation and workup, (b) simulation, treatment planning, and treatment, and (c) follow-up. Twenty-two rectal cancer specific measures were developed (19 quality, 1 aspirational, and 2 surveillance). In addition, dose-volume histogram constraints for conventional and hypofractionated radiation therapy were created. ConclusionsThe quality measures and dose-volume histogram for rectal cancer serves as a guideline to assess the quality of care for patients with rectal cancer receiving radiation therapy. These quality measures will be used for quality surveillance for veterans receiving care both inside and outside the VA system to improve the quality of care for these patients.

Testing a Novel Inpatient Respiratory Depression Electronic Clinical Quality Measure (eCQM) for Orthopedic Practice in Two Large U.S. Health Systems.

The objective of this study was to assess the feasibility of using an electronic clinical quality measure (eCQM) to assess inpatient respiratory depression rates following elective primary total hip or total knee arthroplasty using data routinely collected in electronic health records. Measure testing was conducted at two large urban, academic health systems - Mass General Brigham and a geographically distant system in southern U.S. The risk-adjusted inpatient respiratory depression rates were 3.83 and 2.73% for the two health systems, respectively. Clinician group rates ranged from 1.40 to 4.35%, demonstrating opportunity for improvement. Both the data and measure specifications showed strong reliability and validity to allow for calculation of accurate and comparable rates of inpatient respiratory depression.

Consensus Quality Measures and Dose Constraints for Head and Neck Cancer with an emphasis on Oropharyngeal and Laryngeal Cancer from the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel

PurposeSafeguarding high-quality care using evidence-based radiation therapy for patients with head and neck cancer is crucial to improving oncologic outcomes, including survival and quality of life. Methods and MaterialsThe Veterans Administration (VA) National Radiation Oncology Program established the VA Radiation Oncology Quality Surveillance Program (VAROQS) to develop clinical quality measures (QM) in head and neck cancer. As part of the development of QM, the VA commissioned, along with the American Society for Radiation Oncology, a blue-ribbon panel comprising experts in head and neck cancer, to develop QM. ResultsWe describe the methods used to develop QM and the final consensus QM, as well as aspirational and surveillance QM, which capture all aspects of the continuum of patient care from initial patient work-up, radiation treatment planning and delivery, and follow-up care, as well as dose volume constraints. ConclusionThese QM are intended for use as part of ongoing quality surveillance for veterans receiving radiation therapy throughout the VA as well as outside the VA. They may also be used by the non-VA community as a basic measure of quality care for head and neck cancer patients receiving radiation.

Outcome oriented clinical research and quality measurement in fracture care

Abstract Objective The paradigm shift according to the principles of Value-Based Healthcare is receiving growing attention. In Orthopedic Trauma care, there are no defined standards to provide an incentive system to recompensate for quality instead of quantity. The definitions of quality from the patient's view have to be validated and included in the reimbursement cycle. In our Department, we now have fully standardized and structured documentation and outcome measurement workflow from the clinical point of view. Methods We set up a Fracture Registry with a REDCap® Database to document every patient with surgical treatment. Included are all surgically treated fractures of the extremities, excluding the hand. Furthermore pelvic, acetabular, and periprosthetic fractures, joint dislocations, and tendon ruptures. With interfaces to our Clinical Documentation Systems, the administrative effort became very efficient. The interfaces provide a possibility to identify every patient with inclusion criteria and implement reminder feedback for Surgeons. We collect PROMs with the heartbeat-Software, and document CROMs with REDCap®. We merged these data using the software ‘R’. The documentation of each patient is completed with a database for Adverse Events. Results Data-entry rates were less than 50% in 2018 and have reached 99.1% for 2020 and 99.5% for 2021 for surgically treated fracture patients. Until the submission of this abstract, 2382 surgical procedures have been performed at our Department on 2073 hospitalized patients. 972 patients were included in the Fracture Registry (47%). In a feasibility study, we have merged clinical data with the PROMs of patients treated for distal radius (DR) and proximal humerus (PH) fractures. The baseline values for DR of the quality-of-life questionnaire EQ5D was 0.83 (n=144), after 3months 0.9 (n=58) and 0.97 one year after treatment (n=6). Although not more than a trend, we realized the same pattern for PH-patients with an EQ5D-Baseline of 0.75 (n=63), three months values of 0.75 (n=58), and one-year results of 0.87 (n=6). Conclusion Collecting structured data allows combining routine clinical data with PROMs and CROMs. This information is of inestimable value to define the quality of care for Orthopedic Trauma Surgery. Our combination of Databases to collect diagnosis, subjective and objective outcome measures has been proven feasible and showed high data quality.

Measuring improvement in psoriasis and dermatitis: The development of pruritus clinical quality measures

To the Editor: The Medicare Access and CHIP Reauthorization Act of 2015 created the Merit-based Incentive Payment System (MIPS). Physicians can demonstrate quality and receive incentives by reporting data in performance measures. The Performance Measurement Committee (PMC) of the American Academy of Dermatology (AAD) creates measures specific to dermatology, covering a range of elements of practice.

Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality.

The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.

Association of wound healing with quality and continuity of care and sociodemographic characteristics

To evaluate the association between clinics' wound healing performance and clinic-level measures of care continuity, clinical quality, and sociodemographic characteristics of the population in their catchment areas. In this cross-sectional analysis, we analyzed electronic health records for 180,336 chronic wounds from 480 wound care clinics during the 2018 calendar year. We measured healing performance using a clinic's observed to expected (O/E) ratio, which is based on the rate at which chronic wounds were predicted to heal within 12 weeks given its case mix and the actual healing rate. We compared the top and bottom quintiles, in terms of the O/E ratio, of clinics. Multivariable regression was used to estimate the effect of the clinic-level measures on the O/E ratio. Clinics in the top quintile had higher rates of care continuity and quality measures, as well as a lower proportion of disadvantaged populations in their catchment areas. In the regression model, 10% increases in a clinic's rate of weekly provider visits, nurse visits, and debridement were associated with 2.5%, 3.0% and 0.7% increases, respectively, in the O/E ratio. The weekly provider visit rate had a greater marginal effect when the proportion of African American residents in the clinic's catchment area was larger. Clinic-level measures of care continuity, clinical quality, and sociodemographic composition of their catchment areas' population explain a meaningful part of differences in clinics' wound healing performance. Better care continuity appears to have a greater beneficial effect in disadvantaged populations.

Measuring Inconsistency in Quality Across Patient Groups to Target Quality Improvement.

Quality improvement (QI) may be aimed at improving care for all patients, or it may be targeted at only certain patient groups. Health care providers have little guidance when determining when targeted QI may be preferred. The aim was to develop a method for quantifying performance inconsistency and guidelines for when inconsistency indicates targeted QI, which we apply to the performance of health plans for different patient groups. Retrospective analysis of 7 Health Care Effectiveness Data and Information Set (HEDIS) measures of clinical care quality. All Medicare Advantage (MA) beneficiaries eligible for any of 7 HEDIS measures 2015-2018. MA plans with higher overall performance tended to be less inconsistent in their performance (r=-0.2) across groups defined by race-and-ethnicity and low-income status (ie, dual eligibility for Medicaid or receipt of Low-Income Subsidy). Plan characteristics were usually associated with only small differences in inconsistency. The characteristics associated with differences in consistency [eg, size, Health Maintenance Organization (HMO) status] were also associated with differences in overall performance. We identified 9 (of 363) plans that had large inconsistency in performance across groups (>0.8 SD) and investigated the reasons for inconsistency for 2 example plans. This newly developed inconsistency metric may help those designing and evaluating QI efforts to appropriately determine when targeted QI is preferred. It can be used in settings where performance varies across groups, which can be defined by patient characteristics, geographic areas, hospital wards, etc. Effectively targeting QI efforts is essential in today's resource-constrained health care environment.

How do clinical quality and patient satisfaction vary with provider size in primary care? Evidence from English general practice panel data

We examine the relationship between general practice list size and measures of clinical quality and patient satisfaction. Using data on all English practices from 2005/6 to 2016/17, we estimate practice level models with rich data on patient demographics, deprivation, and morbidity. We use lagged list size to allow for potential simultaneity bias from the effect of quality on list size. We compare results from three different estimation methods: pooled ordinary least squares, random practice effects, fixed practice effects. With all three estimation methods increased list size is associated with reductions in all four measures of patient satisfaction. Increases in list size are associated with worse performance on three clinical quality indicators and better performance on three, though the precision and size of the associations varies with the estimation method. The absolute values of the elasticities of the ten quality indicators with respect to list size are small: in all cases a 10% change in list size would change quality by less than 1%. The lack of evidence that large practices have markedly better quality suggests that encouraging practices to form larger, but looser, groupings, may not, in itself, improve their performance.

Clinical Quality Measures: A Challenge for-and to-Family Physicians.

Improving design, selection and implementation of appropriate clinical quality measures can reduce harms and costs of health care and improve the quality and experience of care delivery. These measures have not been evaluated for appropriateness for use in performance measurement in a systematic, reproducible, and widely accepted manner. We defined 10 criteria for evaluation of measure appropriateness in 4 domains: Patient-centeredness of outcomes, specification of population measured and measure detail, reliable evidence that benefits likely outweigh harms, and independence from significant confounders. We applied these criteria to 24 measures under consideration for statewide use in Massachusetts in public and private incentive-based programs. We appraised each measure as Appropriate or Not Appropriate for such use. We rated 15 measures as Appropriate (62.5%). Three measures (12.5%) were considered Appropriate only if applied at a system level but not for patient-provider assessment and 6 measures (25%) were rated Not Appropriate. Reasons for designation as "Not Appropriate" included benefits not clearly outweighing harms, lack of preservation of patient autonomy, inappropriate specification of population and measure detail, confounding by locus of control, and confounding by social determinants of health. Using this consensus-driven, 10-criteria methodology we were able to evaluate appropriateness of clinical quality measures. This methodology may improve measure design and inform selection of the most appropriate measures for use in quality measurement, financial incentives, and reporting.

Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives.

Electronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings. This study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices. A qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors. Cooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs. Cooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Association Between Provider-Sponsored Health Plan Ownership and Health Insurance Marketplace Plan Quality.

Over 15.3 million Americans relied on the individual health insurance market for health coverage in 2021. Yet, little is known about the relationships between the organizational characteristics of individual market health insurers and quality of coverage, particularly with respect to clinical outcomes. To examine variation in marketplace insurers' quality performance and investigate how performance varies by insurer organizational characteristics. Retrospective cohort study. 381 insurer products, representing 184 unique insurers in 50 states in 2019 and 2020. Marketplace plan clinical quality measures reported in the 2019-2020 CMS Plan Quality Rating System dataset and insurer-product organizational attributes identified from several data sources, including non-profit ownership, Blue Cross Blue Shield Association membership, Medicaid focus and whether or not the insurer product is vertically integrated with a provider organization. Among the 381 insurer products in this study, 35% are part of a provider-sponsored health plan (PSHP) and 70% of these entities received four stars or above for overall quality performance. Overall, PSHPs exhibited higher quality than non-PSHPs for both clinical quality management (0.36 increased on a 5-point scale; 95% CI = 0.11 to 0.62; P = 0.005) and enrollee experience (0.27; 95% CI = 0.03 to 0.50; P = 0.03) summary indicators. Medicaid focused insurers were associated with lower performance on enrollee experience, plan administration, and various outcomes related to clinical quality. Provider-sponsored health plans in the health insurance marketplaces are associated with higher-quality care, as measured by CMS clinical quality measures.