Screening for Elder Abuse in the Veterans Health Administration: Varied Approaches Across a National Health System.

BACKGROUND: Elder abuse (EA) is common and has devastating health consequences yet is rarely detected by healthcare professionals. While EA screening tools exist, little is known about if and how these tools are implemented in real-world clinical settings. The Veterans Health Administration (VHA) has experience screening for, and resources to respond to, other forms of interpersonal violence and may provide valuable insights into approaches for EA screening. OBJECTIVE: Describe EA screening practices across a national integrated healthcare system serving a large population of older adults at risk for EA. DESIGN: Survey of all 139 VHA medical centers from January to August 2021. PARTICIPANTS: Surveys were completed by the Social Work Chief, or delegate, at each site. MAIN MEASURES: The survey assessed the presence and characteristics of EA-specific screening practices as well as general abuse/neglect screening conducted with patients of all ages, including older adults. Follow-up emails were sent to sites that reported screening requesting additional details not included in the initial survey. KEY RESULTS: Overall, 130 sites (94%) responded. Among respondents, 5 (4%) reported screening older adults for EA using a previously published tool, while 6 (5%) reported screening for EA with an unstudied or locally developed tool. Forty-eight percent reported screening patients of all ages for general abuse/neglect using unstudied questions/tools, and 44% reported no EA screening at their site. Characteristics of screening programs (e.g., frequency, clinical setting, provider type) varied widely between sites, as did respondents' understanding of the definition of screening. CONCLUSIONS: High variability in screening practices for abuse/neglect and lack of EA-specific screening in a system that has successfully deployed other standardized screening approaches present an important opportunity to standardize and improve EA detection practices. Lessons learned in VHA could help advance the evidence base for EA screening more broadly to increase overall detection rates for EA nationally.

Impact of a Dyadic Intervention on Family Supporter Involvement in Helping Adults Manage Type 2 Diabetes.

BACKGROUND: Family support for adults' diabetes care is associated with improved self-management and outcomes, but healthcare providers lack structured ways to engage those supporters. OBJECTIVE: Assess the impact of a patient-supporter diabetes management intervention on supporters' engagement in patients' diabetes care, support techniques, and caregiving experience. DESIGN: Multivariate regression models examined between-group differences in support-related measures observed as part of a larger trial randomizing participants to a dyadic intervention versus usual care. PARTICIPANTS: A total of 239 adults with type 2 diabetes and either A1c >8% or systolic blood pressure >160mmHg enrolled with a family supporter. INTERVENTION: Health coaches provided training on positive support techniques and facilitated self-management information sharing and goal-setting. MAIN MEASURES: Patient and supporter reports at baseline and 12 months of supporter roles in diabetes care and caregiving experience. RESULTS: At 12 months, intervention-assigned patients had higher odds of reporting increased supporter involvement in remembering medical appointments (AOR 2.74, 95% CI 1.44, 5.21), performing home testing (AOR 2.40, 95% CI 1.29, 4.46), accessing online portals (AOR 2.34, 95% CI 1.29, 4.30), deciding when to contact healthcare providers (AOR 2.12, 95% CI 1.15, 3.91), and refilling medications (AOR 2.10, 95% CI 1.14, 3.89), but not with attending medical appointments or with healthy eating and exercise. Intervention-assigned patients reported increased supporter use of autonomy supportive communication (+0.27 points on a 7-point scale, p=0.02) and goal-setting techniques (+0.30 points on a 5-point scale, p=0.01). There were no differences at 12 months in change scores measuring supporter distress about patients' diabetes or caregiving burden. Intervention-assigned supporters had significantly larger increases in satisfaction with health system support for their role (+0.88 points on a 10-point scale, p=0.01). CONCLUSIONS: A dyadic patient-supporter intervention led to increased family supporter involvement in diabetes self-management and increased use of positive support techniques, without increasing caregiver stress.

My Goals Are Not Their Goals: Barriers and Facilitators to Delivery of Patient-Centered Care for Patients with Multimorbidity.

BACKGROUND: Patient-centered care reflecting patient preferences and needs is integral to high-quality care. Individualized care is important for psychosocially complex or high-risk patients with multiple chronic conditions (i.e., multimorbidity), given greater potential risks of interventions and reduced benefits. These patients are increasingly prevalent in primary care. Few studies have examined provision of patient-centered care from the clinician perspective, particularly from primary care physicians serving in integrated, patient-centered medical home settings within the US Veterans Health Administration. OBJECTIVE: We sought to clarify facilitators and barriers perceived by primary care physicians in the Veterans Health Administration to delivering patient-centered care for high-risk or complex patients with multimorbidity. DESIGN: We conducted semi-structured telephone interviews from April to July 2020 among physicians across 20 clinical sites. Findings were analyzed with deductive content analysis based on conceptual models of patient-centeredness and hierarchical factors affecting care delivery. PARTICIPANTS: Of 23 physicians interviewed, most were female (n = 14/23, 61%), serving in hospital-affiliated outpatient clinics (n = 14/23, 61%). Participants had a mean of 21 (SD = 11.3) years of experience. KEY RESULTS: Facilitators included the following: effective physician-patient communication to individualize care, prioritize among multiple needs, and elicit goals to improve patient engagement; access to care, enabled by interdisciplinary teams, and dictating personalized care planning; effortful but worthwhile care coordination and continuity; meeting complex needs through effective teamwork; and integrating medical and non-medical care aspects in recognition of patients' psychosocial contexts. Barriers included the following: intra- and interpersonal (e.g., perceived patient reluctance to engage in care); organizational (e.g., limited encounter time); and community or policy impediments (e.g., state decisional capacity laws) to patient-centered care. CONCLUSIONS: Physicians perceived individual physician-patient interactions were the greatest facilitators or barriers to patient-centered care. Efforts to increase primary care patient-centeredness for complex or high-risk patients with multimorbidity could focus on targeting physician-patient communication and reducing interpersonal conflict.

Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention.

BACKGROUND: The ability of latent class models to identify clinically distinct groups among high-risk patients has been demonstrated, but it is unclear how healthcare data can inform group-specific intervention design. OBJECTIVE: Examine how utilization patterns across latent groups of high-risk patients provide actionable information to guide group-specific intervention design. DESIGN: Cohort study using data from 2012 to 2015. PATIENTS: Participants were 934,787 patients receiving primary care in the Veterans Health Administration, with predicted probability of 12-month hospitalization in the top 10th percentile during 2014. MAIN MEASURES: Patients were assigned to latent groups via mixture-item response theory models based on 28 chronic conditions. We modeled odds of all-cause mortality, hospitalizations, and 30-day re-hospitalizations by group membership. Detailed outpatient and inpatient utilization patterns were compared between groups. KEY RESULTS: A total of 764,257 (81.8%) of patients were matched with a comorbidity group. Groups were characterized by substance use disorders (14.0% of patients assigned), cardiometabolic conditions (25.7%), mental health conditions (17.6%), pain/arthritis (19.1%), cancer (15.3%), and liver disease (8.3%). One-year mortality ranged from 2.7% in the Mental Health group to 14.9% in the Cancer group, compared to 8.5% overall. In adjusted models, group assignment predicted significantly different odds of each outcome. Groups differed in their utilization of multiple types of care. For example, patients in the Pain group had the highest utilization of in-person primary care, with a mean (SD) of 5.3 (5.0) visits in the year of follow-up, while the Substance Use Disorder group had the lowest, with 3.9 (4.1) visits. The Substance Use Disorder group also had the highest rates of using services for housing instability (25.1%), followed by the Liver group (10.1%). CONCLUSIONS: Latent groups of high-risk patients had distinct hospitalization and utilization profiles, despite having comparable levels of predicted baseline risk. Utilization profiles pointed towards system-specific care needs that could inform tailored interventions.

Association of Integrated Mental Health Services with Physical Health Quality Among VA Primary Care Patients.

BACKGROUND: Integrated care for comorbid depression and chronic medical disease improved physical and mental health outcomes in randomized controlled trials. The Veterans Health Administration (VA) implemented Primary Care-Mental Health Integration (PC-MHI) across all primary care clinics nationally to increase access to mental/behavioral health treatment, alongside physical health management. OBJECTIVE: To examine whether widespread, pragmatic PC-MHI implementation was associated with improved care quality for chronic medical diseases. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included 828,050 primary care patients with at least one quality metric among 396 VA clinics providing PC-MHI services between October 2013 and September 2016. MAIN MEASURE(S): For outcome measures, chart abstractors rated whether diabetes and cardiovascular quality metrics were met for patients at each clinic as part of VA's established quality reporting program. The explanatory variable was the proportion of primary care patients seen by integrated mental health specialists in each clinic annually. Multilevel logistic regression models examined associations between clinic PC-MHI proportion and patient-level quality metrics, adjusting for regional, patient, and time-level effects and clinic and patient characteristics. KEY RESULTS: Median proportion of patients seen in PC-MHI per clinic was 6.4% (IQR=4.7-8.7%). Nineteen percent of patients with diabetes had poor glycemic control (hemoglobin A1c >9%). Five percent had severely elevated blood pressure (>160/100 mmHg). Each two-fold increase in clinic PC-MHI proportion was associated with 2% lower adjusted odds of poor glycemic control (95% CI=0.96-0.99; p=0.046) in diabetes. While there was no association with quality for patients diagnosed with hypertension, patients without diagnosed hypertension had 5% (CI=0.92-0.99; p=0.046) lower adjusted odds of having elevated blood pressures. CONCLUSIONS AND RELEVANCE: Primary care clinics where integrated mental health care reached a greater proportion of patients achieved modest albeit statistically significant gains in key chronic care quality metrics, providing optimism about the expected effects of large-scale PC-MHI implementation on physical health.

Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report.

BACKGROUND: Segmentation models such as latent class analysis are an increasingly popular approach to inform group-tailored interventions for high-risk complex patients. Multiple studies have identified clinically meaningful high-risk segments, but few have evaluated change in groupings over time. OBJECTIVES: To describe population-level and individual change over time in latent comorbidity groups among Veterans at high-risk of hospitalization in the Veterans Health Administration (VA). RESEARCH DESIGN: Using a repeated cross-sectional design, we conducted a latent class analysis of chronic condition diagnoses. We compared latent class composition, patient high-risk status, and patient class assignment in 2018 to 2020. SUBJECTS: Two cohorts of eligible patients were selected: those active in VA primary care and in the top decile of predicted one-year hospitalization risk in 2018 (n = 951,771) or 2020 (n = 978,771). MEASURES: Medical record data were observed from January 2016-December 2020. Latent classes were modeled using indicators for 26 chronic health conditions measured with a 2-year lookback period from study entry. RESULTS: Five groups were identified in both years, labeled based on high prevalence conditions: Cardiometabolic (23% in 2018), Mental Health (18%), Substance Use Disorders (16%), Low Diagnosis (25%), and High Complexity (10%). The remaining 8% of 2018 patients were not assigned to a group due to low predicted probability. Condition prevalence overall and within groups was stable between years. However, among the 563,725 patients identified as high risk in both years, 40.8% (n = 230,185) had a different group assignment in 2018 versus 2020. CONCLUSIONS: In a repeated latent class analysis of nearly 1 million Veterans at high-risk for hospitalization, population-level groups were stable over two years, but individuals often moved between groups. Interventions tailored to latent groups need to account for change in patient status and group assignment over time.

Subgroups of High-Risk Veterans Affairs Patients Based on Social Determinants of Health Predict Risk of Future Hospitalization.

OBJECTIVE: Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death. STUDY DESIGN AND SETTING: SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression. RESULTS: Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14). CONCLUSION: Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events.

Diabetes Distress Among Dyads of Patients and Their Health Supporters: Links With Functional Support, Metabolic Outcomes, and Cardiac Risk.

BACKGROUND: Patients with diabetes (PWD) often experience diabetes distress which is associated with worse self-management and glycemic control. In contrast, PWD who receive support from family and friends (supporters) have better diabetes outcomes. PURPOSE: To examine the associations of PWD diabetes distress and supporters' distress about PWDs' diabetes with supporters' roles and PWD cardiometabolic outcomes. METHODS: We used baseline data from 239 adults with Type 2 diabetes and their supporters participating in a longitudinal trial. PWD and supporter diabetes distress (high vs. low) were determined using the Problem Areas in Diabetes Scale-5. Outcomes included PWD-reported help from supporters with self-care activities, supporter-reported strain, PWD metabolic outcomes (glycemic control [HbA1c], systolic blood pressure [SBP], and non-HDL cholesterol) and 5 and 10 year risk of cardiac event (calculated using the United Kingdom Prospective Diabetes Study algorithm). RESULTS: PWDs with high diabetes distress were more likely to report that their supporters helped with taking medications, coordinating medical care, and home glucose testing (p's < .05), but not more likely to report help with diet or exercise. High supporter distress was associated with greater supporter strain (p < .001). High supporter diabetes distress was associated with higher PWD HbA1c (p = .045), non-HDL cholesterol (p = .011), and 5 (p = .002) and 10 year (p = .001) cardiac risk. CONCLUSIONS: Adults with high diabetes distress report more supporter help with medically focused self-management but not with diet and exercise. Supporter distress about PWD diabetes was consistently associated with worse outcomes. PWD diabetes distress had mixed associations with their diabetes outcomes.

Autonomy support from informal health supporters: links with self-care activities, healthcare engagement, metabolic outcomes, and cardiac risk among Veterans with type 2 diabetes.

This study examined the role of autonomy support from adults' informal health supporters (family or friends) in diabetes-specific health behaviors and health outcomes. Using baseline data from 239 Veterans with type 2 diabetes at risk of complications enrolled in behavioral trial, we examined associations between autonomy support from a support person and that support person's co-residence with the participant's diabetes self-care activities, patient activation, cardiometabolic measures, and predicted risk of a cardiac event. Autonomy support from supporters was associated with significantly increased adherence to healthy lifestyle behaviors (diet, p < .001 and exercise, p = .003); higher patient activation (p < .001); greater patient efficacy in interacting with healthcare providers, and lower 5-year (p = .044) and 10-year (p = .027) predicted cardiac risk. Autonomy support was not significantly associated with diabetes-specific behaviors (checking blood glucose, foot care, or medication taking); or hemoglobin A1c, systolic blood pressure, or non-HDL cholesterol. There was a significant interaction of autonomy support and supporter residence in one model such that lack of autonomy support was associated with lower patient activation only among individuals with in-home supporters. No other interactions were significant. Findings suggest that autonomy support from family and friends may play a role in patient self-management, patient activation, and lower cardiac risk.

Association of the Implementation of the Patient-Centered Medical Home with Quality of Life in Patients with Multimorbidity.

BACKGROUND: The patient-centered medical home (PCMH) has clinical benefits for chronic disease care, but the association with patient-reported outcomes such as health-related quality of life (HRQoL) is unexplored in patients with multimorbidity (two or more chronic diseases). OBJECTIVE: To examine if greater clinic-level PCMH implementation was associated with higher HRQoL in multimorbid adults. DESIGN: A retrospective cohort study. PARTICIPANTS: Twenty-two thousand ninety-five multimorbid patients who received primary care at 944 Veterans Health Administration (VHA) clinics. MAIN MEASURES: Our exposure was the Patient Aligned Care Team Implementation Progress Index (PI2) for the clinic in 2012, a previously validated composite measure of PCMH implementation. Higher PI2 scores indicate better performance within eight PCMH domains. Outcomes were patient-reported HRQoL measured by the physical and mental component scores (PCS and MCS) from the Short Form-12 patient experiences survey in 2013-2014. Interaction of the outcomes with total hospitalizations and primary care visit count was also examined. Generalized estimating equations were used for main models after adjusting for patient and clinic characteristics. RESULTS: The cohort average age was 68 years, mostly male (96%), and had an average of 4.4 chronic diagnoses. Compared with patients seen at the lowest scoring clinics for PCMH implementation, care in the highest scoring clinics was associated with a higher adjusted marginal mean PCS (42.3 (95% CI 41.3-43.4) versus 40.3 (95% CI 39.1-41.5), P = 0.01), but a lower MCS (35.2 (95% CI 34.4-36.1) versus 36.0 (95% CI 35.3-36.8), P = 0.17). Patients with prior hospitalizations seen in clinics with higher compared with lower PI2 scores had a 2.7 point greater MCS (95% CI 0.6-4.8; P = 0.01). CONCLUSIONS: Multimorbid patients seen in clinics with greater PCMH implementation reported higher physical HRQoL, but lower mental HRQoL. The association between PCMH implementation and mental HRQoL may depend on complex interactions with disease severity and prior hospitalizations.

Association of the Patient-Centered Medical Home Implementation with Chronic Disease Quality in Patients with Multimorbidity.

BACKGROUND: The patient-centered medical home (PCMH) was established in part to improve chronic disease management, yet evidence is limited for effects on patients with multimorbidity. OBJECTIVE: To examine the association of Patient-Aligned Care Team (PACT) implementation, the Veterans Health Administration (VA) PCMH model, and care quality for multimorbid patients enrolled in VA primary care from 2012 to 2014. DESIGN: Retrospective cohort. PATIENTS: 318,764 multimorbid (> 3 chronic diseases) patients receiving care in 917 clinics. MAIN MEASURES: PCMH implementation was measured using the PACT Implementation Progress Index (PI2) for clinics in 2012. The PI2 is a validated composite measure of administrative and survey data with higher scores associated with greater care quality. Quality outcomes from 2013 to 2014 were assessed from External Peer Review Program (EPRP) metrics. Outcomes included preventative care, chronic disease management, and mental health and substance use metrics. We used generalized estimating equations to model associations adjusting for patient and clinic characteristics. We also examined associations for a subgroup with > 5 chronic diseases. KEY RESULTS: For one-third of metrics (5/15), greater implementation of PACT in 2012 was associated with higher predicted probability of meeting the quality metric in 2013-2014. This association persisted for only two metrics (diabetic glycemic control, P < 0.001; lipid control in ischemic heart disease, P = 0.02) among patients with > 5 chronic diseases. CONCLUSIONS: Multimorbid patients engaged in care from clinics with higher PCMH implementation received higher quality care across several quality domains, but this association was reduced in patients with > 5 chronic diseases.

Diagnosis and Care of Chronic Health Conditions Among Medicaid Expansion Enrollees: a Mixed-Methods Observational Study.

BACKGROUND: It is uncertain how Medicaid expansion under the Affordable Care Act influences the diagnosis of chronic health conditions, and the care and health of enrollees with chronic conditions. OBJECTIVE: Describe the prevalence of new and pre-existing chronic health conditions among Medicaid expansion enrollees. Examine whether perceived changes in specific types of access and self-rated health status differed between enrollees with chronic conditions and those without. Examine how gaining Medicaid coverage affected chronic disease management and well-being. DESIGN: Mixed-methods study including a telephone survey and semi-structured interviews. SETTING: Michigan's Medicaid expansion, the "Healthy Michigan Plan" (HMP). PARTICIPANTS: 4090 survey respondents (response rate 54%) with ≥ 12 months HMP enrollment and 67 interviewees with ≥ 6 months enrollment. MAIN MEASURES: Self-reported chronic condition diagnoses, changes in physical/mental health, and healthcare access. Descriptive survey data were adjusted for survey design and nonresponse. Semi-structured interview questions about how gaining HMP coverage led to changes in health status. KEY RESULTS: Among enrollees, 68% had a self-reported diagnosis of a chronic health condition; 42% of those were newly diagnosed since HMP enrollment. In multivariable models, enrollees with chronic conditions were significantly more likely to report improved physical (adjusted odds ratio (aOR) 1.70, 95% CI (1.40, 2.07)) and mental health (aOR 1.75, (1.43, 2.15)) since HMP enrollment than enrollees without chronic conditions. Among enrollees with chronic conditions, the strongest predictors of improvements in health were having seen a primary care physician, improved mental health care access, and improved medication access. Interviewees with chronic conditions described how increased access to health care led to improvements in both physical and mental health. CONCLUSIONS: Enrollees with expanded Medicaid coverage commonly reported detection of previously undiagnosed chronic conditions. Perceived health status and access improved more often among enrollees with chronic health conditions. Improved access was associated with improved physical and mental health among this vulnerable group.

Changes in Health and Ability to Work Among Medicaid Expansion Enrollees: a Mixed Methods Study.

BACKGROUND: Michigan expanded Medicaid under the Affordable Care Act (Healthy Michigan Plan [HMP]) to improve the health of low-income residents and the state's economy. OBJECTIVE: To understand HMP's impact on enrollees' health, ability to work, and ability to seek employment DESIGN: Mixed methods study, including 67 qualitative interviews and 4090 computer-assisted telephone surveys (response rate 53.7%) PARTICIPANTS: Non-elderly adult HMP enrollees MAIN MEASURES: Changes in health status, ability to work, and ability to seek employment KEY RESULTS: Half (47.8%) of respondents reported better physical health, 38.2% better mental health, and 39.5% better dental health since HMP enrollment. Among employed respondents, 69.4% reported HMP helped them do a better job at work. Among out-of-work respondents, 54.5% agreed HMP made them better able to look for a job. Among respondents who changed jobs, 36.9% agreed HMP helped them get a better job. In adjusted analyses, improved health was associated with the ability to do a better job at work (aOR 4.08, 95% CI 3.11-5.35, p < 0.001), seek a job (aOR 2.82, 95% CI 1.93-4.10, p < 0.001), and get a better job (aOR 3.20, 95% CI 1.69-6.09, p < 0.001), but not with employment status (aOR 1.08, 95% CI 0.89-1.30, p = 0.44). In interviews, several HMP enrollees attributed their ability to get or maintain employment to improved physical, mental, and dental health because of services covered by HMP. Remaining barriers to work cited by enrollees included older age, disability, illness, and caregiving responsibilities. CONCLUSIONS: Many low-income HMP enrollees reported improved health, ability to work, and job seeking after obtaining health insurance through Medicaid expansion.

Comparing the Barriers and Facilitators of Heart Failure Management as Perceived by Patients, Caregivers, and Clinical Providers.

BACKGROUND: Heart failure (HF) management requires the participation of patients, their significant others, and clinical providers. Each group may face barriers to HF management that may be unique or may overlap. OBJECTIVE: The aim of this study was to compare the barriers and facilitators of HF management as perceived by patients, significant others, and clinical providers. METHODS: Participants were recruited from a Veterans Health Administration facility. Eligible patients had a diagnosis of HF (ICD9 code 428.XX), 1 or more HF-related visit in the previous year, and a significant other who was their primary caregiver. Significant others were adults with no history of cognitive impairments caring for patients with HF. Providers were eligible if they cared for patients with HF. All participants completed semistructured interviews designed to elicit barriers to managing HF and strategies that they used to overcome these barriers. Interviews were transcribed and analyzed using latent thematic analysis, and recruitment continued until thematic saturation was attained. RESULTS: A total of 17 couples and 12 providers were recruited. All 3 groups identified poor communication as a key barrier to HF management, including communication between patients and their significant other, between couples and providers, and providers with each other. Significant others noted that the lack of direct communication with clinical providers hindered their efforts to care for the patient. All 3 groups emphasized the importance of family members in optimizing adherence to HF self-management recommendations. CONCLUSIONS: Providers, patients, and significant others all play important and distinct roles in the management of HF. Tools to enhance communication and collaboration for all 3 and supporting the needs of significant others are missing components of current HF care.

Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories.

OBJECTIVE: Many healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and characterize latent subgroups of high-risk patients based on risk score trajectories. STUDY DESIGN: Observational study of 7289 patients discharged from Veterans Health Administration (VA) hospitals during a 1-week period in November 2012 and categorized in the top 5th percentile of risk for hospitalization. METHODS: Using VA administrative data, we calculated weekly risk scores using the validated Care Assessment Needs model, reflecting the predicted probability of hospitalization. We applied the non-parametric k-means algorithm to identify latent subgroups of patients based on the trajectory of patients' hospitalization probability over a 2-year period. We then compared baseline sociodemographic characteristics, comorbidities, health service use, and social instability markers between identified latent subgroups. RESULTS: The best-fitting model identified two subgroups: moderately high and persistently high risk. The moderately high subgroup included 65% of patients and was characterized by moderate subgroup-level hospitalization probability decreasing from 0.22 to 0.10 between weeks 1 and 66, then remaining constant through the study end. The persistently high subgroup, comprising the remaining 35% of patients, had a subgroup-level probability increasing from 0.38 to 0.41 between weeks 1 and 52, and declining to 0.30 at study end. Persistently high-risk patients were older, had higher prevalence of social instability and comorbidities, and used more health services. CONCLUSIONS: On average, one third of patients initially identified as high risk stayed at very high risk over a 2-year follow-up period, while risk for the other two thirds decreased to a moderately high level. This suggests that multiple approaches may be needed to address high-risk patient needs longitudinally or intermittently.

Survey Instruments to Assess Patient Experiences With Access and Coordination Across Health Care Settings: Available and Needed Measures.

BACKGROUND: Improving access can increase the providers a patient sees, and cause coordination challenges. For initiatives that increase care across health care settings, measuring patient experiences with access and care coordination will be crucial. OBJECTIVES: Map existing survey measures of patient experiences with access and care coordination expected to be relevant to patients accessing care across settings. Preliminarily examine whether aspects of access and care coordination important to patients are represented by existing measures. RESEARCH DESIGN: Structured literature review of domains and existing survey measures related to access and care coordination across settings. Survey measures, and preliminary themes from semistructured interviews of 10 patients offered VA-purchased Community Care, were mapped to identified domains. RESULTS: We identified 31 existing survey instruments with 279 items representing 6 access and 5 care coordination domains relevant to cross-system care. Domains frequently assessed by existing measures included follow-up coordination, primary care access, cross-setting coordination, and continuity. Preliminary issues identified in interviews, but not commonly assessed by existing measures included: (1) acceptability of distance to care site given patient's clinical situation; (2) burden on patients to access and coordinate care and billing; (3) provider familiarity with Veteran culture and VA processes. CONCLUSIONS: Existing survey instruments assess many aspects of patient experiences with access and care coordination in cross-system care. Systems assessing cross-system care should consider whether patient surveys accurately reflect the level of patients' concerns with burden to access and coordinate care, and adequately reflect the impact of clinical severity and cultural familiarity on patient preferences.

Patient-Centered Medical Home Implementation in the Veterans Health Administration and Primary Care Use: Differences by Patient Comorbidity Burden.

BACKGROUND: The patient-centered medical home (PCMH) model has several components to improve care for patients with high comorbidity, including greater access to face-to-face primary care. OBJECTIVE: We examined whether high-comorbidity patients had larger increases in primary care provider (PCP) visits attributable to PCMH implementation in a large integrated health system relative to other patients enrolled in primary care. DESIGN, SUBJECTS AND MAIN MEASURES: This longitudinal study examined a 1 % random sample of 9.3 million patients enrolled in the Veterans Health Administration (VHA) at any time between 2003 and 2013. Face-to-face visits with PCPs per quarter were identified through VHA administrative data. Comorbidity was measured using the Gagne index and patients with a weighted score of ≥ 2 were defined as high comorbidity. We applied interrupted time-series models to estimate marginal changes in PCP visits attributable to PCMH implementation. Differences in marginal changes were calculated across comorbidity groups (high vs. low). Analyses were stratified by age group to account for Medicare eligibility. KEY RESULTS: Among age 65+ patients, PCMH was associated with greater PCP visits starting four and ten quarters following implementation for high- and low-comorbidity patients, respectively. Changes were larger for high-comorbidity patients (eight to 11 greater visits per 1000 patients per quarter). Among patients age < 65, PCMH was associated with greater visits for high-comorbidity patients starting eight quarters following implementation, but fewer visits for low-comorbidity patients in all quarters. The difference in visit changes across groups ranged from 18 to 67 visits per 1000 patients per quarter. CONCLUSIONS: Increases in PCP visits attributable to PCMH were greater among patients with higher comorbidity. Health systems implementing PCMH should account for population-level comorbidity burden when planning for PCMH-related changes in PCP utilization.

Effects of performance measure implementation on clinical manager and provider motivation.

BACKGROUND: Clinical performance measurement has been a key element of efforts to transform the Veterans Health Administration (VHA). However, there are a number of signs that current performance measurement systems used within and outside the VHA may be reaching the point of maximum benefit to care and in some settings, may be resulting in negative consequences to care, including overtreatment and diminished attention to patient needs and preferences. Our research group has been involved in a long-standing partnership with the office responsible for clinical performance measurement in the VHA to understand and develop potential strategies to mitigate the unintended consequences of measurement. OBJECTIVE: Our aim was to understand how the implementation of diabetes performance measures (PMs) influences management actions and day-to-day clinical practice. DESIGN: This is a mixed methods study design based on quantitative administrative data to select study facilities and quantitative data from semi-structured interviews. PARTICIPANTS: Sixty-two network-level and facility-level executives, managers, front-line providers and staff participated in the study. APPROACH: Qualitative content analyses were guided by a team-based consensus approach using verbatim interview transcripts. A published interpretive motivation theory framework is used to describe potential contributions of local implementation strategies to unintended consequences of PMs. KEY RESULTS: Implementation strategies used by management affect providers' response to PMs, which in turn potentially undermines provision of high-quality patient-centered care. These include: 1) feedback reports to providers that are dissociated from a realistic capability to address performance gaps; 2) evaluative criteria set by managers that are at odds with patient-centered care; and 3) pressure created by managers' narrow focus on gaps in PMs that is viewed as more punitive than motivating. CONCLUSIONS: Next steps include working with VHA leaders to develop and test implementation approaches to help ensure that the next generation of PMs motivate truly patient-centered care and are clinically meaningful.