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The quality of care experienced by members of racial and ethnic minority groups in Medicare Advantage, which is an increasingly important source of Medicare coverage for these groups, has critical implications for health equity. Comparing gaps in Medicare Advantage and traditional Medicare for three quality-of-care outcomes, measured by adverse health events, between minority and non-Hispanic White populations, we found that the relative magnitude of the gaps varied both by racial and ethnic minority group and by quality measure. Hispanic versus non-Hispanic White gaps were smaller in Medicare Advantage than in traditional Medicare for all outcomes: avoidable emergency department use, preventable hospitalizations, and thirty-day hospital readmissions. The gap between non-Hispanic Black and non-Hispanic White populations was larger in Medicare Advantage than in traditional Medicare for avoidable emergency department use but was no different for hospital readmissions and was smaller for preventable hospitalizations. The Asian versus non-Hispanic White gap was similar in Medicare Advantage and traditional Medicare for avoidable emergency department use and preventable hospitalizations but was larger in Medicare Advantage for hospital readmissions. As Medicare Advantage enrollment expands, monitoring the quality of care for enrollees who are members of racial and ethnic minority groups will remain important.
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Ethnicity , Medicare Part C , Aged , United States , Humans , Minority Groups , State Medicine , Universal Health Insurance , Quality of Health CareABSTRACT
OBJECTIVE: To study diagnosis coding intensity across Medicare programs, and to examine the impacts of changes in the risk model adopted by the Centers for Medicare and Medicaid Services (CMS) for 2024. DATA SOURCES AND STUDY SETTING: Claims and encounter data from the CMS data warehouse for Traditional Medicare (TM) beneficiaries and Medicare Advantage (MA) enrollees. STUDY DESIGN: We created cohorts of MA enrollees, TM beneficiaries attributed to Accountable Care Organizations (ACOs), and TM non-ACO beneficiaries. Using the 2019 Hierarchical Condition Category (HCC) software from CMS, we computed HCC prevalence and scores from base records, then computed incremental prevalence and scores from health risk assessments (HRA) and chart review (CR) records. DATA COLLECTION/EXTRACTION METHODS: We used CMS's 2019 random 20% sample of individuals and their 2018 diagnosis history, retaining those with 12 months of Parts A/B/D coverage in 2018. PRINCIPAL FINDINGS: Measured health risks for MA and TM ACO individuals were comparable in base records for propensity-score matched cohorts, while TM non-ACO beneficiaries had lower risk. Incremental health risk due to diagnoses in HRA records increased across coverage cohorts in line with incentives to maximize risk scores: +0.9% for TM non-ACO, +1.2% for TM ACO, and + 3.6% for MA. Including HRA and CR records, the MA risk scores increased by 9.8% in the matched cohort. We identify the HCC groups with the greatest sensitivity to these sources of coding intensity among MA enrollees, comparing those groups to the new model's areas of targeted change. CONCLUSIONS: Consistent with previous literature, we find increased health risk in MA associated with HRA and CR records. We also demonstrate the meaningful impacts of HRAs on health risk measurement for TM coverage cohorts. CMS's model changes have the potential to reduce coding intensity, but they do not target the full scope of hierarchies sensitive to coding intensity.
Subject(s)
Accountable Care Organizations , Centers for Medicare and Medicaid Services, U.S. , Clinical Coding , Medicare , Risk Adjustment , Humans , United States , Risk Adjustment/methods , Male , Aged , Female , Medicare/statistics & numerical data , Accountable Care Organizations/statistics & numerical data , Aged, 80 and over , Medicare Part C/statistics & numerical data , Risk Assessment , Insurance Claim Review , Reimbursement, Incentive/statistics & numerical dataABSTRACT
Hmong-Americans experience higher rates of diabetes and poorer diabetes-related health outcomes than their White peers. Traditional methods of diabetes education do not reach Hmong patients effectively due to known socioeconomic and literacy barriers. The purpose of this study is to examine the acceptability of a culturally informed diabetes self-management education video tool, using digital storytelling that was created using a community-engaged approach, administered in a single academic clinic that sees a large percentage of Hmong patients. The video tool was successful in the areas of acceptability, story transformation, and story identification; 96% of participants stated that the video felt like something from their community, 88% stated that they could identify with the story, 79% stated that they wanted to know what happened next, and 70% of participants reported that they were motivated to do something different after watching. New methods to improve diabetes education and improve health outcomes in Hmong communities are needed. Culturally informed digital storytelling is one tool, which may be used to improve diabetes health outcomes in this population.
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Objective: To determine the clinical effect of personal continuous glucose monitoring (CGM) in a diverse population with type 2 diabetes (T2D). Research Design and Methods: A report was created from the electronic health record identifying adults prescribed CGM at an urban family medicine clinic between January 1, 2019, and February 23, 2022. An "index date" was identified as the start of CGM. The closest hemoglobin A1c (A1c) 6 months or more after the index date was identified as the "follow-up date." The primary outcome of this study was to compare the percentage of individuals meeting the MN Community Measure (MNCM) D5 HbA1c goal of <8% at the follow-up date versus the index date. Results: Seventy-two patients were identified after the exclusion criteria were applied. Approximately one-third of patients required utilization of an interpreter and 76% of patients were of a racial or ethnic minority. The mean HbA1c prior to CGM use was 9.8%, with 16.7% of the population meeting the MNCM D5 A1c goal of <8%. At the follow-up date, the mean A1c was 8.4% (mean difference -1.4%; p < 0.001), with 41.7% of the population meeting goal (mean difference +25%; p < 0.001). Subgroup analyses affirm that the results of the primary outcome were sustained despite insulin use status. Conclusion: A diverse population with T2D had a significant reduction in A1c and was more likely to meet the MNCM D5 A1c goal of <8% after an average of 6 months using personal CGM.
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PURPOSE: To learn how the highest-performing primary care practices manage change when implementing improvements to diabetes care delivery. METHODS: We ranked a total of 330 primary care practices submitting practice management assessments and diabetes reports to the Understanding Infrastructure Transformation Effects on Diabetes study in 2017 and 2019 by Optimal Diabetes Care performance. We ranked practices from the top quartile by greatest annual improvement to capture dynamic change. Starting with the top performers, we interviewed practice leaders to identify their most effective strategies for managing change. Interview transcripts were qualitatively analyzed to identify change management strategies. Saturation occurred when no new strategies were identified over 2 consecutive interviews. RESULTS: Ten of the top 13 practices agreed to interviews. We identified 199 key comments representing 48 key care management concepts. We also categorized concepts into 6 care management themes and 37 strategic approaches. We categorized strategic approaches into 13 distinct change management strategies. The most common strategies identified were (1) standardizing the care process, (2) performance awareness, (3) enhancing care teams, (4) health care organization participation, (5) improving reporting systems, (6) engaging staff and clinicians, (7) accountability for tasks, (8) engaging leadership, and (9) tracking change. Care management themes identified by most practices included proactive care, improving patient relationships, and previsit planning. CONCLUSIONS: Top-performing primary care practices identify a similar group of strategies as important for managing change during quality improvement activities. Practices involved in diabetes improvement activities, and perhaps other chronic conditions, should consider adopting these change management strategies.
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Change Management , Diabetes Mellitus , Humans , Primary Health Care , Delivery of Health Care , Diabetes Mellitus/therapy , Quality ImprovementABSTRACT
Medicare Advantage (MA) is a rapidly growing source of coverage for Medicare beneficiaries. Examining how MA performs compared with traditional Medicare is an important policy issue. We analyzed national MA encounter data and found that the adjusted differences in resource use between MA and traditional Medicare varied widely across medical conditions in 2019. Total resource use in MA was generally lower than in traditional Medicare but by varying amounts across conditions, and it was not significantly different from traditional Medicare for some conditions. This variation was explained by resource use for hospital inpatient services in MA relative to traditional Medicare. Resource use for treatments was considerably lower in MA than in traditional Medicare across all conditions, whereas resource use for imaging and testing was consistently higher in MA for all conditions. As MA grows, efforts are needed to identify mechanisms driving differences in resource use between MA and traditional Medicare and to assess their implications for patient care.
Subject(s)
Medicare Part C , Aged , United States , Humans , PolicyABSTRACT
Medicare Advantage (MA) plans increase their risk-adjusted payments through intensive coding in health risk assessments (HRAs) and chart reviews. Whether the additional diagnoses from HRAs and chart reviews are associated with increased resource use is not known. Using national MA encounter data (2016-2019), we examine the relative contributions of three health risk scores to MA resource use: the base risk score that excludes diagnoses from HRAs and chart reviews; the incremental score added to the base score from diagnoses in HRAs; and the incremental score added from diagnoses in chart reviews. We find that the incremental risk scores explain 53.5% to 64.5% of resource use relative to the base risk score effect-that is, 35.5% to 46.5% of the incremental risk scores are not accompanied by increased resource use. While HRAs and chart reviews contribute to more complete coding of diagnoses, they are sources of intensive coding not accompanied by resource use.
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Medicare Part C , Aged , Humans , United States , Risk Assessment , Risk Adjustment , Risk FactorsABSTRACT
OBJECTIVE: Identify the improvement in diabetes performance measures and population-based clinical outcomes resulting from changes in care management processes (CMP) in primary care practices over 3 years. RESEARCH DESIGN AND METHODS: This repeated cross-sectional study tracked clinical performance measures for all diabetes patients seen in a cohort of 330 primary care practices in 2017 and 2019. Unit of analysis was patient-year with practice-level CMP exposures. Causal inference is based on dynamic changes in individual CMPs between years by practice. We used the Bayesian method to simultaneously estimate a five-outcome model: A1c, systolic and diastolic blood pressure, guideline-based statin use, and Optimal Diabetes Care (ODC). We control for unobserved time-invariant practice characteristics and secular change. We modeled correlation of errors across outcomes. Statistical significance was identified using 99% Bayesian credible intervals (analogous to P < 0.01). RESULTS: Implementation of 18 of 62 CMPs was associated with statistically significant improvements in patient outcomes. Together, these resulted in 12.1% more patients meeting ODC performance measures. Different CMPs affected different outcomes. Three CMPs accounted for 47% of the total ODC improvement, 68% of A1c decrease, 21% of SBP reduction, and 55% of statin use increase: 1) systems for identifying and reminding patients due for testing, 2) after-visit follow-up by a nonclinician, and 3) guideline-based clinician reminders for preventive services during a clinic visit. CONCLUSIONS: Effective quality improvement in primary care focuses on practice redesign that clearly improves diabetes outcomes. Tailoring CMP adoption in primary care provides effective improvement in ODC performance through focused changes in diabetes outcomes.
Subject(s)
Diabetes Mellitus , Hydroxymethylglutaryl-CoA Reductase Inhibitors , Humans , Cross-Sectional Studies , Glycated Hemoglobin , Bayes Theorem , Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use , Diabetes Mellitus/therapyABSTRACT
CONTEXT: Osteopathic manipulative treatment (OMT) for the allopathic resident is an elective at the University of Minnesota North Memorial Residency that engages the resident in the basic tenants of osteopathic medicine, with exposure to the vast application of OMT with a curricular focus on low back pain management. Implementing an elective curriculum is a feasible way to improve attitudes in OMT for MDs in a Family Medicine residency, and residents can learn OMT in an elective rotation. OBJECTIVES: This article aims to determine if MDs who complete an OMT for the allopathic physician elective rotation have higher comfort caring for patients with back pain compared to those who do not complete the elective. Further, this article is designed to evaluate if these MDs continue to incorporate OMT into the care they provide once they graduate from their residency programs. METHODS: Graduates from the University of Minnesota North Memorial Family Medicine Residency (2013 to 2019) were sent an email invitation in August 2020 to complete a Qualtrics survey regarding their comfort with caring for patients with back pain, referral patterns for these patients, and the ongoing use of OMT in their practices. Doctor of Osteopathic Medicine (DO) graduates who responded to the survey were removed from the analysis. RESULTS: Among emailed graduates, 61.8% (42/68) completed the survey, with representation from each class ranging from 1 to 7 years postresidency. The five DO graduates who responded were removed from the analysis. Among the remaining 37 respondents, 27 had completed the OMT for the allopathic rotation ("elective participants") during their residency training and 10 had not ("control"). Half (50.0%) of the control group provide OMT care compared to 66.7% of the elective participants, with a comfort score of 22.6 (standard deviation [SD] 32.7) in the control group vs. 34.0 (SD 21.0) in elective participants (on a 0-100 scale; 100 being completely comfortable; p=0.091). Among the control group, 40.0% regularly refer to a DO provider compared to 66.7% of those who completed the elective (p=0.257). The mean comfort score for performing a physical examination on patients presenting with back pain was 78.7 (SD 13.1) and 80.9 (SD 19.3) in the control and elective participants groups, respectively (p=0.198). CONCLUSIONS: Allopathic Family Medicine residents who completed an elective rotation in OMT have a slight increase in frequency of referring to DOs. They also have a meaningful increase in comfort performing OMT. With the limited number of DOs being a common barrier to OMT care, more widely implemented training in OMT for allopathic Family Medicine residents may be a reasonable intervention to improve the care of patients with back pain.
Subject(s)
Internship and Residency , Low Back Pain , Manipulation, Osteopathic , Osteopathic Medicine , Humans , Osteopathic Medicine/education , Education, Medical, Graduate , Low Back Pain/therapyABSTRACT
PURPOSE: To learn whether the COVID-19 pandemic's disruptions and associated reduced health outcomes for people with chronic conditions might have been caused by a decrease in care management processes (CMPs) in primary care clinics METHODS: Longitudinal cohort design with repeated survey-based measures of CMPs from 2017, 2019, and 2021 in 269 primary care clinics in Minnesota. RESULTS: There were only small differences in organizational characteristics and no differences in overall CMPs between the 269 clinics analyzed and the 287 that only completed surveys in 1 or 2 years. Overall CMP scores rose by similar amounts (1.6% and 2.1%) from 2017 to 2019 and from 2019 to 2021. In 2021, CMP scores were lower in small medical groups than in large medical groups in 2017 (66.1% vs 78.5%, P <.001), a similar difference to that in 2017. Care management process scores were also lower in clinics in urban areas compared with rural areas (73.9% vs 79.0%, P <.001), but overall scores in all subgroups were higher in 2021 than in 2017. This improvement occurred despite reports from 55% of clinic leaders that the pandemic had been very or extremely disruptive. CONCLUSIONS: Although quite disrupted by the pandemic, care management processes for chronic disease care in these resilient primary care clinics actually increased from 2019 to 2021, at least in clinics that were part of large organizations. However, that was not true for clinics from smaller groups and perhaps for other areas of care.
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COVID-19 , Pandemics , Humans , Ambulatory Care Facilities , Minnesota , Chronic Disease , Primary Health CareABSTRACT
Thirty-minute office blood pressure (OBP-30) is an alternative to ambulatory blood pressure (BP) measurement, yet is impractical to implement. This study aimed to determine whether unattended BP readings over 15 minutes would result in a similar probability of obtaining a BP of <140/90. Sixty-seven adults self-described as having high BP were analyzed. BP was measured at baseline and every 5 minutes for 15 minutes with the initial reading compared to the average of the last three readings (OBP-15). Compared to baseline, there was a decline in both average systolic (4.2 points) and diastolic (2.8 points) BP. The probability of BP control predicted by multivariate model was 71.6% at baseline and 78.0% using OBP-15 (p=0.011). The increase in BP control from initial to OBP-15 measurement was significant for indigenous or persons of color compared to whites, and men compared to women. OBP-15 is convenient and results in lower BP readings and higher probability of BP control compared to the initial reading.
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INTRODUCTION: Current research shows no increased risk of thromboembolic events with mild COVID-19 but does not account for comorbidities. The aim of this study was to examine the incidence of thromboembolic events, including pulmonary embolism, cerebral infarction, and deep vein thrombosis, in nonhospitalized patients diagnosed with COVID-19 while accounting for comorbidities such as diabetes, asthma, COPD and cancer. METHODS: We completed a large retrospective observational analysis of adult patients within a large urban health system. RESULTS: Using a logit framework (with and without propensity score weighting), there was no increased risk of thromboembolic events among patients positive for SARS-CoV-2 who did not require hospitalization for COVID-19. CONCLUSION: This data suggest prophylactic anticoagulation is likely not warranted in the outpatient setting.
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COVID-19 , Adult , Humans , COVID-19/diagnosis , COVID-19/epidemiology , SARS-CoV-2 , Anticoagulants , Retrospective Studies , COVID-19 Testing , HospitalizationABSTRACT
OBJECTIVE: To complement the previously illustrated method to measure resource use in Medicare Advantage (MA) using Encounter data and provide technical details and SAS code to validate Encounter data and implement resource use measures in MA. DATA SOURCES: 2015-2018 MA Encounter, Medicare Provider Analysis and Review (MedPAR), Healthcare Effectiveness Data and Information System (HEDIS), and Traditional Medicare (TM) claims data. STUDY DESIGN: Secondary data analysis. DATA COLLECTION/EXTRACTION METHODS: We select MA contracts with high data completeness (≤10% missing hospital stays in Encounter data and ≤±10% difference in ambulatory and emergency department visits between Encounter and HEDIS data). We randomly sample TM beneficiaries with a similar geographic distribution as MA enrollees in the selected contracts. We develop standardized prices of services using TM payments, and we measure MA resource use for inpatient, outpatient, Part D, and hospice services. PRINCIPAL FINDINGS: We report identifiers/names of contracts with high data completeness. We provide SAS code to manage Encounter data, develop standardized prices, and measure MA resource use. CONCLUSIONS: Greater use and validation of Encounter data can help improve data quality. Our results can be used to inform studies using Encounter data to learn about MA performance.
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Medicare Part C , Aged , Humans , Inpatients , Length of Stay , United StatesABSTRACT
OBJECTIVES: To compare primary care management processes (CMPs) and outcome measures for diabetes quality among large, medium, and small medical groups. STUDY DESIGN: Observational comparison of differences in processes and outcomes over time among 329 primary care practices that agreed to participate and returned completed surveys in both 2017 and 2019. METHODS: We used a standardized composite measure of diabetes quality along with its 5 components and a survey measure of the presence of systematic CMPs to compare the outcomes and processes of care among clinics that were in large (≥ 12 sites), medium (4-11 sites), and small (1-3 sites) medical groups. RESULTS: Practices from large groups had better performance than those in medium and small groups on the composite measure of diabetes outcomes in 2017 (46.5 vs 40.6 and 34.4, respectively; P < .001), as well as on each of the 5 component measures. They also had more CMPs in place (74.2% vs 66.9% and 61.4%; P < .001), including the 10 CMPs that are associated with the highest level of performance (84.2% vs 77.9% and 72.2%; P < .001). However, repeated measures in 2019 showed that the smaller groups had gained on both quality and CMP measures. There was also substantial overlap on both CMPs and performance among practices in groups of different sizes. CONCLUSIONS: On average, primary care practices that are part of large well-established medical groups outperformed smaller-sized groups in diabetes care quality, probably because they have the resources, leadership, and infrastructure to provide more consistent care through more organized CMPs.
Subject(s)
Diabetes Mellitus , Quality of Health Care , Diabetes Mellitus/therapy , Humans , Outcome Assessment, Health Care , Surveys and QuestionnairesABSTRACT
A popular method for estimating a causal treatment effect with observational data is the difference-in-differences model. In this work, we consider an extension of the classical difference-in-differences setting to the hierarchical context in which data cannot be matched at the most granular level. Our motivating example is an application to assess the impact of primary care redesign policy on diabetes outcomes in Minnesota, in which the policy is administered at the clinic level and individual outcomes are not matched from pre- to post-intervention. We propose a Bayesian hierarchical difference-in-differences model, which estimates the policy effect by regressing the treatment on a latent variable representing the mean change in group-level outcome. We present theoretical and empirical results showing a hierarchical difference-in-differences model that fails to adjust for a particular class of confounding variables, biases the policy effect estimate. Using a structured Bayesian spike-and-slab model that leverages the temporal structure of the difference-in-differences context, we propose and implement variable selection approaches that target sets of confounding variables leading to unbiased and efficient estimation of the policy effect. We evaluate the methods' properties through simulation, and we use them to assess the impact of primary care redesign of clinics in Minnesota on the management of diabetes outcomes from 2008 to 2017.
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Bayes Theorem , Causality , Computer Simulation , Confounding Factors, Epidemiologic , MinnesotaABSTRACT
OBJECTIVE: To check the completeness of Medicare Advantage (MA) Encounter data and to illustrate a process to measure resource use among MA enrollees using Encounter data. DATA SOURCES: 2015 Preliminary MA Encounter, Medicare Provider Analysis and Review (MedPAR), Healthcare Effectiveness Data and Information System (HEDIS), and 2013 Traditional Medicare (TM) claims data. STUDY DESIGN: Secondary data analysis. DATA COLLECTION/EXTRACTION METHODS: We calculated the percentage of each contract's total hospitalizations in Encounter data after identifying total inpatient stays from Encounter and MedPAR data. We constructed each contract's ambulatory visits and emergency department (ED) visits per 1000 enrollees using Encounter data and compared those visit counts with the counts from HEDIS. We defined high data completeness as having less than 10% missing hospital stays and less than ±10% difference in ambulatory and ED visits between Encounter and HEDIS data. We used TM payments as standardized prices of services to examine resource use among MA enrollees with cancer in the contracts with high data completeness. PRINCIPAL FINDINGS: We identified 83 of 380 MA contracts with high data completeness. Total resource use per enrollee with cancer in the 83 contracts was $14,715 in 2015. Service-specific resource use was $5342 for inpatient care, $5932 for professional services and $3441 for outpatient facility services. These represent what an MA enrollee with cancer would have cost on average if MA plans paid providers at TM payment rates, holding the observed utilization constant. CONCLUSIONS: Checking the completeness of Encounter data is an important step to ensure the validity of research on MA resource use. Using Encounter data to measure MA resource use is feasible. It can compensate for the lack of payment information in Encounter data. It will be important to identify and refine ways to best use Encounter data to learn about care provision to MA enrollees.
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Insurance, Health/statistics & numerical data , Medicare Part C/standards , Patient Acceptance of Health Care/statistics & numerical data , Aged , Datasets as Topic , Humans , Outcome Assessment, Health Care/statistics & numerical data , United StatesABSTRACT
BACKGROUND: Previous studies have shown benefits of 30-minute office blood pressure (OBP-30) but did not report on race and gender. The purpose of this study was to determine if similar effects are seen in a diverse urban population. METHODS: Patients with diabetes and/or cardiovascular disease (age 18-89) were invited to participate. Blood pressure was measured using standard procedure (SOBP). Patients were left alone in an exam room connected to an automated office blood pressure monitor which obtained BP readings every 5 minutes for 30 minutes. The last 5 measurements were averaged for the OBP-30 measurement. Primary outcomes were BPs measured using SOBP and OBP-30. Multivariate logit methods were used to estimate the average probability of having a BP measured <140/90 mm Hg (BPM <140/90) for the 2 measurement methods. Differences were computed across methods, in total and by sex and race, all other factors held constant. RESULTS: The adjusted probability of having a BPM <140/90 was 47.1% using SOBP and 66.7% using OBP-30 (P < 0.01). Using SOBP, females had a 26.2 PP lower probability of having a BPM <140/90 (P < 0.001) than males. Relative to white patients, Black patients had a 43.9 PP lower (P < 0.001) and other races a 38.5 PP lower (P < 0.001) probability of having a BPM <140/90 using SOBP. Using OBP-30, these differences narrowed and became statistically insignificant. CONCLUSIONS: OBP-30 may increase the probability of BPM meeting goals, especially in females and patients who are Black, indigenous, or persons of color.
Subject(s)
Cardiovascular Diseases , Hypertension , Adolescent , Adult , Aged , Aged, 80 and over , Blood Pressure , Blood Pressure Determination/methods , Blood Pressure Monitoring, Ambulatory/methods , Female , Humans , Hypertension/diagnosis , Male , Middle Aged , Urban Population , Young AdultABSTRACT
PURPOSE: The aim of this study was to determine what strategies and factors are most important for high performance in the primary care of patients with diabetes. METHODS: We performed a mixed-methods, cross-sectional, observational analysis of interviews and characteristics of primary care clinics in Minnesota and bordering areas. We compared strategies, facilitators, and barriers identified by 31 leaders of 17 clinics in high-, middle-, and low-performance quartiles on a standardized composite measure of diabetes outcomes for 416 of 586 primary care clinics. Semistructured interview data were combined with quantitative data regarding clinic performance and a survey of the presence of care management processes. RESULTS: The interview analysis identified 10 themes providing unique insights into the factors and strategies characterizing the 3 performance groups. The main difference was the degree to which top-performing clinics used patient data to guide proactive and outreach methods to intensify treatment and monitor effect. Top clinics also appeared to view visit-based care management processes as necessary but insufficient, whereas all respondents regarded being part of a large system as mostly helpful. CONCLUSIONS: Top-performing clinic approaches to diabetes care differ from lower-performing clinics primarily by emphasizing data-driven proactive outreach to patients to intensify treatment. Although confirmatory studies are needed, clinical leaders should consider the value of this paradigm shift in approach to care.
Subject(s)
Delivery of Health Care/methods , Diabetes Mellitus/therapy , Primary Health Care/methods , Quality of Health Care , Ambulatory Care Facilities , Cross-Sectional Studies , Humans , Interviews as Topic , Organizational Culture , Qualitative Research , Surveys and QuestionnairesABSTRACT
OBJECTIVE: To identify the impact of changes surrounding certification as a patient-centered medical home (PCMH) on outcomes for patients with diabetes. STUDY SETTING: Minnesota legislation established mandatory quality reporting for patients with diabetes and statewide standards for certification as a PCMH. Patient-level quality reporting data (2008-2018) were used to study the impact of transition to a PCMH. STUDY DESIGN: Achievement of Minnesota's optimal diabetes care standard-in aggregate and by component-was modeled for adult patients with Type 1 or Type 2 diabetes as a function of time relative to the year the patient's primary care practice achieved PCMH certification. Patients from uncertified practices were used to control for general trend. Practice-level random effects captured time-invariant characteristics of practices and the practices' average patient. DATA COLLECTION: Electronic health record data were submitted by 695 Minnesota practices capturing components of the quality standard: blood sugar control, cholesterol control, blood pressure control, nonsmoking status, and use of aspirin. PRINCIPAL FINDINGS: The first cohort of practices achieving PCMH certification (July 2010-June 2014) showed statistically insignificant changes in optimal care. The next cohort of practices (July 2014-June 2018) achieved larger, clinically meaningful increases in quality of care during the time prior to and following certification. Specifically, this second cohort of practices was estimated to achieve a 12.8 percentage-point improvement (P < .001) in the predicted probability of providing optimal diabetes care over the period spanning 3 years before to 3 years after certification. CONCLUSIONS: Our results suggest that the initial cohort of certified practices was already performing at a high level before certification, perhaps requiring little change in their operations to achieve PCMH certification. The second cohort, on the other hand, made meaningful, quality-improving changes in the years surrounding certification. Differences by cohort may partially explain the inconsistent PCMH impacts found in the literature.