Transforming Primary Care in New York Through Patient-Centered Medical Homes: Findings From Qualitative Research.

BACKGROUND: The patient-centered medical home (PCMH) model, an important component of healthcare transformation in the United States, is an approach to primary care delivery with the goal of improving population health and the patient care experience while reducing costs. PCMH research most often focuses on system level indicators including healthcare use and cost; descriptions of patient and provider experience with PCMH are relatively sparse and commonly limited in scope. This study, part of a mixed-methods evaluation of a multi-year New York State initiative to refine and expand the PCMH model, describes patient and provider experience with New York State PCMH and its key components. METHODS: The qualitative component of the evaluation included focus groups with patients of PCMH practices in 5 New York State counties (n = 9 groups and 67 participants) and interviews with providers and practice administrators at New York State PCMH practices (n = 9 interviews with 10 participants). Through these focus groups and interviews, we elicited first-person descriptions of experiences with, as well as perspectives on, key components of the New York State PCMH model, including accessibility, expanded use of electronic health records, integration of behavioral health care, and care coordination. RESULTS: There was evident progress and some satisfaction with the PCMH model, particularly regarding integrated behavioral health and, to some extent, expanded use of electronic health records. There was less evident progress with respect to improved access and reasonable wait times, which caused patients to continue to use urgent care or the emergency department as substitutes for primary care. CONCLUSIONS: It is critical to understand the strengths and limitations of the PCMH model, so as to continue to improve upon and promote it. Strengths of the model were evident to participants in this study; however, challenges were also described. It is important to note that these challenges are difficult to separate from wider healthcare system issues, including inadequate incentives for value-based care, and carry implications for PCMH and other models of healthcare delivery.

Sleep Duration and Health Care Expenditures in the United States.

OBJECTIVE: To estimate the average incremental health care expenditures associated with habitual long and short duration of sleep as compared with healthy/average sleep duration. DATA SOURCE: Medical Expenditure Panel Survey data (2012; N=6476) linked to the 2010-2011 National Health Interview Survey. STUDY DESIGN: Annual differences in health care expenditures are estimated for habitual long and short duration sleepers as compared with average duration sleepers using 2-part logit generalized linear regression models. PRINCIPAL FINDINGS: Habitual short duration sleepers reported an additional $1400 in total unadjusted health care expenditures compared to people with average sleep duration (P<0.01). After adjusting for demographics, socioeconomic factors, and health behavior factors, this difference remained significant with an additional $1278 in total health care expenditures over average duration sleepers (P<0.05). Long duration sleepers reported even higher, $2994 additional health care expenditures over average duration sleepers. This difference in health care expenditures remained significantly high ($1500, P<0.01) in the adjusted model. Expenditure differences are more pronounced for inpatient hospitalization, office expenses, prescription expenses, and home health care expenditures. CONCLUSIONS: Habitual short and long sleep duration is associated with higher health care expenditures, which is consistent with the association between unhealthy sleep duration and poorer health outcomes.

Developing population health scientists: Findings from an evaluation of the Robert Wood Johnson Foundation Health & Society Scholars Program.

•RWJF Health & Society Scholars (HSS) program outcomes evaluated.•HSS alumni have higher scholarly productivity and impact than control group.•HSS alumni are more engaged in population health research than controls.•HSS alumni and controls are similar on other outcome measures.•Training programs can be evaluated with adequate attention to selection bias.

Decoding Nonadherence to Hypertensive Medication in New York City: A Population Segmentation Approach.

OBJECTIVE: Nearly one-third of adults in New York City (NYC) have high blood pressure and many social, economic, and behavioral factors may influence nonadherence to antihypertensive medication. The objective of this study is to identify profiles of adults who are not taking antihypertensive medications despite being advised to do so. METHODS: We used a machine learning-based population segmentation approach to identify population profiles related to nonadherence to antihypertensive medication. We used data from the 2016 NYC Community Health Survey to identify and segment adults into subgroups according to their level of nonadherence to antihypertensive medications. RESULTS: We found that more than 10% of adults in NYC were not taking antihypertensive medications despite being advised to do so by their health care providers. We identified age, neighborhood poverty, diabetes, household income, health insurance coverage, and race/ethnicity as important characteristics that can be used to predict nonadherence behaviors as well as used to segment adults with hypertension into 10 subgroups. CONCLUSIONS: Identifying segments of adults who do not adhere to hypertensive medications has practical implications as this knowledge can be used to develop targeted interventions to address this population health management challenge and reduce health disparities.