“Hopes and wishes”: Goals of high-need, high-cost older patients and their caregivers

Wyman, Mary F.; Liebzeit, Daniel; Voils, Corrine I.; Bowers, Barbara J.; Chapman, Elizabeth; Gilmore‐Bykovskyi, Andrea; Kennelty, Korey A.; Kind, Amy; Loosen, Julia; Rogus-Pulia, Nicole; Dattalo, Melissa

doi:10.1016/j.pec.2020.02.022

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…CMP enrollees experienced lower hospitalization rates compared to controls (21.0%, vs. 24.2%) (Dorr et al, 2008), while GRACE enrollees in a Veterans Affairs Medical Center (VAMC) in Indiana had 7.1% fewer ED visits, 14.8% fewer 30-readmissions, 37.9% fewer inpatient admissions (Schubert et al, 2016) Despite this substantial evidence base, the need to divert older adults from using ED services for non-urgent reasons continues as does the need to prevent unwanted hospitalizations, hospital readmissions, and prolonged NF placements for persons with few care needs. The missed opportunity to provide patient-centered care not only impacts quality of life (Wyman et al, 2020), it also contributes to increasing costs for state Medicaid programs as older adults who are near-poor, medically at-risk, and do not receive coordinated care are more likely to become qualified beneficiaries (Rudowitz et al, 2021;Spillman & Kemper, 1995;Spillman and Waidmann, 2014). This is particularly true for persons who live in rural geographic areas where care coordination and community-based supportive services may not be as readily available (Dill et al, 2023;Franzosa et al, 2023;Liebzeit et al, 2022).…”

Section: Care Coordination: Aligning Provider Practices With Personal...mentioning

confidence: 99%

Critical Elements of Care Coordination for Older Persons in Rural Communities: An Evaluation of the Iowa Return to Community Service Demonstration

Kaskie,

Shehu,

Ohms

et al. 2023

J Appl Gerontol

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We evaluated the Iowa Return to Community, a service demonstration designed to coordinate care and reduce preventable healthcare utilization among at-risk older persons living at home in rural communities. During 2021, 262 older persons elected to participate in the IRTC program. Individuals who participated were more likely to live in micropolitan areas (OR = 2.30, 95% CI 1.34–3.95) relative to metropolitan locations. Individuals who used recommended services were more likely to be men (OR 3.65, 95% CI 1.16–11.51) and more likely to live in rural (OR 17.48, 95% CI 1.37–223.68) and micropolitan areas (OR 3.17, 95% CI 1.00–10.05). However, prevention of health care use corresponded more with consistent and prolonged IRTC program engagement rather than volume of service use. The IRTC constitutes a population aging and rural health strategy to reduce unnecessary health care use while supporting individual preferences to remain at home.

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Section: Care Coordination: Aligning Provider Practices With Personal...mentioning

confidence: 99%

Critical Elements of Care Coordination for Older Persons in Rural Communities: An Evaluation of the Iowa Return to Community Service Demonstration

Kaskie,

Shehu,

Ohms

et al. 2023

J Appl Gerontol

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Regaining Autonomy in a Holding Environment: Patients’ Perspectives on the Existential Communication with Physicians When Suffering from a Severe, Chronic Illness: A Qualitative Nordic Study

2022

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Identifying future high healthcare utilization in patients with multimorbidity – development and internal validation of machine learning prediction models using electronic health record data

Weil,

Zwerwer,

Chu

et al. 2024

Health Technol.

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Purpose To develop and internally validate prediction models with machine learning for future potentially preventable healthcare utilization in patients with multiple long term conditions (MLTC). This study is the first step in investigating whether prediction models can help identify patients with MLTC that are most in need of integrated care. Methods A retrospective cohort study was performed with electronic health record data from adults with MLTC from an academic medical center in the Netherlands. Based on demographic and healthcare utilization characteristics in 2017, we predicted ≥ 12 outpatient visits, ≥ 1 emergency department (ED) visits, and ≥ 1 acute hospitalizations in 2018. Four machine learning models (elastic net regression, extreme gradient boosting (XGB), logistic regression, and random forest) were developed, optimized, and evaluated in a hold-out dataset for each outcome. Results A total of 14,486 patients with MLTC were included. Based on the area under the curve (AUC) and calibration curves, the XGB model was selected as final model for all three outcomes. The AUC was 0.82 for ≥ 12 outpatient visits, 0.76 for ≥ 1 ED visits and 0.73 for ≥ 1 acute hospitalizations. Despite adequate AUC and calibration, precision-recall curves showed suboptimal performance. Conclusions The final selected models per outcome can identify patients with future potentially preventable high healthcare utilization. However, identifying high-risk patients with MLTC and substantiating if they are most in need of integrated care remains challenging. Further research is warranted investigating whether patients with high healthcare utilization are indeed the most in need of integrated care and whether quantitively identified patients match the identification based on clinicians’ experience and judgment.

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“Hopes and wishes”: Goals of high-need, high-cost older patients and their caregivers

Cited by 7 publications

References 27 publications

Critical Elements of Care Coordination for Older Persons in Rural Communities: An Evaluation of the Iowa Return to Community Service Demonstration

Critical Elements of Care Coordination for Older Persons in Rural Communities: An Evaluation of the Iowa Return to Community Service Demonstration

Regaining Autonomy in a Holding Environment: Patients’ Perspectives on the Existential Communication with Physicians When Suffering from a Severe, Chronic Illness: A Qualitative Nordic Study

Identifying future high healthcare utilization in patients with multimorbidity – development and internal validation of machine learning prediction models using electronic health record data

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