Outcomes of a randomized quality improvement trial for high-risk Veterans in year two

Impact of Referring High-Risk Patients to Intensive Outpatient Primary Care Services: A Propensity Score-Matched Analysis

BACKGROUND

Many healthcare systems have implemented intensive outpatient primary care programs with the hopes of reducing healthcare costs.

OBJECTIVE

The Veterans Health Administration (VHA) piloted primary care intensive management (PIM) for patients at high risk for hospitalization or death, or "high-risk." We evaluated whether a referral model would decrease high-risk patient costs.

DESIGN

Retrospective cohort study using a quasi-experimental design comparing 456 high-risk patients referred to PIM from October 2017 to September 2018 to 415 high-risk patients matched on propensity score.

PARTICIPANTS

Veterans in the top 10th percentile of risk for 90-day hospitalization or death and recent hospitalization or emergency department (ED) visit.

INTERVENTION

PIM consisted of interdisciplinary teams that performed comprehensive assessments, intensive case management, and care coordination services.

MAIN OUTCOMES AND MEASURES

Change in VHA and non-VHA outpatient utilization, inpatient admissions, and costs 12 months pre- and post-index date.

KEY RESULTS

Of the 456 patients referred to PIM, 301 (66%) enrolled. High-risk patients referred to PIM had a marginal reduction in ED visits (- 0.7; [95% CI - 1.50 to 0.08]; p = 0.08) compared to propensity-matched high-risk patients; overall outpatient costs were similar. High-risk patients referred to PIM had similar number of medical/surgical hospitalizations (- 0.2; [95% CI, - 0.6 to 0.16]; p = 0.2), significant increases in length of stay (6.36; [CI, - 0.01 to 12.72]; p = 0.05), and higher inpatient costs ($22,628, [CI, $3587 to $41,669]; p = 0.02) than those not referred to PIM.

CONCLUSIONS AND RELEVANCE

VHA intensive outpatient primary care was associated with higher costs. Referral to intensive case management programs targets the most complex patients and may lead to increased utilization and costs, particularly in an integrated healthcare setting with robust patient-centered medical homes.

TRIAL REGISTRATION

PIM 2.0: Patient Aligned Care Team (PACT) Intensive Management (PIM) Project (PIM2). NCT04521816. https://clinicaltrials.gov/study/NCT04521816.

Non-emergency department (ED) interventions to reduce ED utilization: a scoping review

BACKGROUND

Emergency department (ED) crowding is a global burden. Interventions to reduce ED utilization have been widely discussed in the literature, but previous reviews have mainly focused on specific interventions or patient groups within the EDs. The purpose of this scoping review was to identify, summarize, and categorize the various types of non-ED-based interventions designed to reduce unnecessary visits to EDs.

METHODS

This scoping review followed the JBI Manual for Evidence Synthesis and the PRISMA-SCR checklist. A comprehensive structured literature search was performed in the databases MEDLINE and Embase from 2008 to March 2024. The inclusion criteria covered studies reporting on interventions outside the ED that aimed to reduce ED visits. Two reviewers independently screened the records and categorized the included articles by intervention type, location, and population.

RESULTS

Among the 15,324 screened records, we included 210 studies, comprising 183 intervention studies and 27 systematic reviews. In the primary studies, care coordination/case management or other care programs were the most commonly examined out of 15 different intervention categories. The majority of interventions took place in clinics or medical centers, in patients' homes, followed by hospitals and primary care settings - and targeted patients with specific medical conditions.

CONCLUSION

A large number of studies have been published investigating interventions to mitigate the influx of patients to EDs. Many of these targeted patients with specific medical conditions, frequent users and high-risk patients. Further research is needed to address other high prevalent groups in the ED - including older adults and mental health patients (who are ill but may not need the ED). There is also room for further research on new interventions to reduce ED utilization in low-acuity patients and in the general patient population.

Home Heart Hospital Associated With Reduced Hospitalizations and Costs Among High-Cost Patients With Cardiovascular Disease

BACKGROUND

There is no widely accepted care model for managing high-need, high-cost (HNHC) patients. We hypothesized that a Home Heart Hospital (H3), which provides longitudinal, hospital-level at-home care, would improve care quality and reduce costs for HNHC patients with cardiovascular disease (CVD).

OBJECTIVE

To evaluate associations between enrollment in H3, which provides longitudinal, hospital-level at-home care, care quality, and costs for HNHC patients with CVD.

METHODS

This retrospective within-subject cohort study used insurance claims and electronic health records data to evaluate unadjusted and adjusted annualized hospitalization rates, total costs of care, part A costs, and mortality rates before, during, and following H3.

RESULTS

Ninety-four patients were enrolled in H3 between February 2019 and October 2021. Patients' mean age was 75 years and 50% were female. Common comorbidities included congestive heart failure (50%), atrial fibrillation (37%), coronary artery disease (44%). Relative to pre-enrollment, enrollment in H3 was associated with significant reductions in annualized hospitalization rates (absolute reduction (AR): 2.4 hospitalizations/year, 95% confidence interval [95% CI]: -0.8, -4.0; p < 0.001; total costs of care (AR: -$56 990, 95% CI: -$105 170, -$8810; p < 0.05; and part A costs (AR: -$78 210, 95% CI: -$114 770, -$41 640; p < 0.001). Annualized post-H3 total costs and part A costs were significantly lower than pre-enrollment costs (total costs of care: -$113 510, 95% CI: -$151 340, -$65 320; p < 0.001; part A costs: -$84 480, 95% CI: -$121 040, -$47 920; p < 0.001).

CONCLUSIONS

Longitudinal home-based care models hold promise for improving quality and reducing healthcare spending for HNHC patients with CVD.

Meeting high-risk patient pain care needs through intensive primary care: a secondary analysis

OBJECTIVE

Chronic pain disproportionately affects medically and psychosocially complex patients, many of whom are at high risk of hospitalisation. Pain prevalence among high-risk patients, however, is unknown, and pain is seldom a focus for improving high-risk patient outcomes. Our objective is to (1) evaluate pain frequency in a high-risk patient population and (2) identify intensive management (IM) programme features that patients and providers perceive as important for promoting patient-centred pain care within primary care (PC)-based IM.

DESIGN

Secondary observational analysis of quantitative and qualitative evaluation data from a multisite randomised PC-based IM programme for high-risk patients.

SETTING

Five integrated local Veterans Affairs (VA) healthcare systems within distinct VA administrative regions.

PARTICIPANTS

Staff and high-risk PC patients in the VA.

INTERVENTION

A multisite randomised PC-based IM programme for high-risk patients.

OUTCOME MEASURES

(a) Pain prevalence based on VA electronic administrative data and (b) transcripts of interviews with IM staff and patients that mentioned pain.

RESULTS

Most (70%, 2593/3723) high-risk patients had at least moderate pain. Over one-third (38%, 40/104) of the interviewees mentioned pain or pain care. There were 89 pain-related comments addressing IM impacts on pain care within the 40 interview transcripts. Patient-identified themes were that IM improved communication and responsiveness to pain. PC provider-identified themes were that IM improved workload and access to expertise. IM team member-identified themes were that IM improved pain care coordination, facilitated non-opioid pain management options and mitigated provider compassion fatigue. No negative IM impacts on pain care were mentioned.

CONCLUSIONS

Pain is common among high-risk patients. Future IM evaluations should consider including a focus on pain and pain care, with attention to impacts on patients, PC providers and IM teams.

Association of COVID-19 coinfection with increased mortality among patients with Pseudomonas aeruginosa bloodstream infection in the Veterans Health Administration system

Objective

Pseudomonas aeruginosa bloodstream infection (PA-BSI) and COVID-19 are independently associated with high mortality. We sought to demonstrate the impact of COVID-19 coinfection on patients with PA-BSI.

Design

Retrospective cohort study.

Setting

Veterans Health Administration.

Patients

Hospitalized patients with PA-BSI in pre-COVID-19 (January 2009 to December 2019) and COVID-19 (January 2020 to June 2022) periods. Patients in the COVID-19 period were further stratified by the presence or absence of concomitant COVID-19 infection.

Methods

We characterized trends in resistance, treatment, and mortality over the study period. Multivariable logistic regression and modified Poisson analyses were used to determine the association between COVID-19 and mortality among patients with PA-BSI. Additional predictors included demographics, comorbidities, disease severity, antimicrobial susceptibility, and treatment.

Results

A total of 6,714 patients with PA-BSI were identified. Throughout the study period, PA resistance rates decreased. Mortality decreased during the pre-COVID-19 period and increased during the COVID-19 period. Mortality was not significantly different between pre-COVID-19 (24.5%, 95% confidence interval [CI] 23.3-28.6) and COVID-19 period/COVID-negative (26.0%, 95% CI 23.5-28.6) patients, but it was significantly higher in COVID-19 period/COVID-positive patients (47.2%, 35.3-59.3). In the modified Poisson analysis, COVID-19 coinfection was associated with higher mortality (relative risk 1.44, 95% CI 1.01-2.06). Higher Charlson Comorbidity Index, higher modified Acute Physiology and Chronic Health Evaluation score, and no targeted PA-BSI treatment within 48 h were also predictors of higher mortality.

Conclusions

Higher mortality was observed in patients with COVID-19 coinfection among patients with PA-BSI. Future studies should explore this relationship in other settings and investigate potential SARS-CoV-2 and PA synergy.

Integrating data analytics, peer support, and whole health coaching to improve the health outcomes of homeless veterans: Study protocol for an effectiveness-implementation trial

BACKGROUND

Homelessness is a strong determinant of acute care service utilization (inpatient hospitalization, emergency department visits) among US adults. Data analytics, peer support, and patient-centered approaches can collectively offer high-quality care for homeless patients who frequently utilize acute care ("super utilizers"). However, few outpatient programs have integrated these components and tested their effectiveness for this patient population.

OBJECTIVE

To test the effectiveness and implementation potential of a novel intervention that integrates data analytics with peers trained in whole health coaching ("Peer Whole Health") to reduce use of acute care among homeless adults.

METHODS

Using a randomized controlled trial design at two US Veterans Health Administration Medical Centers, we plan to enroll 220 veterans in primary care on VHA's Homeless Registry who are flagged on a super-utilizer clinical dashboard. Participants will complete a baseline interview, be randomized to Enhanced Usual Care (EUC; primary care and data analytics) or EUC plus 18 sessions of Peer Whole Health over 6 months, and be re-interviewed at 3, 6, and 9 months. Qualitative interviews with primary care staff and patients will identify facilitators and barriers to more widespread implementation of the intervention.

DISCUSSION

The primary hypothesis is that those who receive the intervention will have fewer total days of all-cause hospitalization. If confirmed, the findings can provide healthcare systems that serve homeless super-utilizers with a high-value approach to care that can be integrated into primary care services and reduce overall costs for these patients.

CLINICAL TRIAL REGISTRATION

The study is registered with ClinicalTrials.gov (NCT05176977).

The Effect of Primary Care Visits on Total Patient Care Cost: Evidence From the Veterans Health Administration

BACKGROUND

Since the 1980s, primary care (PC) in the US has been recognized as the backbone of healthcare providing comprehensive care to complex patients, coordinating care among specialists, and rendering preventive services to contain costs and improve clinical outcomes. However, the effect of PC visits on total patient care cost has been difficult to quantify.

OBJECTIVE

To assess the effect of PC visits on total patient care cost.

METHODS

This is a retrospective study of over 5 million patients assigned to a PC provider in the Veterans Health Administration (VHA) in each of the 4 fiscal years (FY 2016-2019). The main outcome of interest is total annual patient care cost. We assessed the effect of primary care visits on total patient care cost first by descriptive statistics, and then by multivariate regressions adjusting for severity of illness and other confounders. We conducted in-depth sensitivity analyses to validate the findings.

RESULTS

On average, each additional in-person PC visit was associated with a total cost reduction of $721 (per patient per year). The first PC visit was associated with the largest savings, $3976 on average, and a steady diminishing return was observed. Further, the higher the patient risk (severity of illness), the larger the cost reduction: Among the top 10% of high-risk patients, the first PC in-person visit was associated with a reduction of $16 406 (19%).

CONCLUSIONS

These findings, substantiated by our exhaustive sensitivity analyses, suggest that expanding PC capacity can significantly reduce overall health care costs and improve patient care outcomes given the former is a strong proxy of the latter.

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.