Development of a prediction model for 30-day acute readmissions among older medical patients: the influence of social factors along with other patient-specific and organisational factors

Predictive analytics support for complex chronic medical conditions: An experience-based co-design study of physician managers' needs and preferences

PURPOSE

The literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND).

METHODS

This qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care.

RESULTS

Interview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation.

CONCLUSIONS

The HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals' involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care.

Nutritional risk predicts readmission within 30 and 180 days after discharge among older adult patients across a broad spectrum of diagnoses

BACKGROUND AND AIMS

Hospital readmissions can have negative consequences for older adult patients, their relatives, the hospital, and society. Previous studies indicate that older adult patients who are at nutritional risk during hospital admission are at higher risk of readmission. There is a lack of studies investigating this relationship across different older adult patient groups while using recommended instruments and adjusting for relevant confounders. Thus, the aim of the present study was to investigate whether nutritional status according to the Nutrition Risk Screening 2002 during hospitalization predicted readmission among older adult patients within 30 and 180 days across a broad spectrum of wards and diagnoses when adjusting for age, sex, length-of-stay, diagnosis, and discharge destination.

MATERIALS AND METHODS

The present study is a retrospective cohort study based on registry data and included 21,807 older adult patients (≥65 years) hospitalized during a 5-year period. In order to investigate the relationship between nutritional risk and readmission, hierarchical logistic regression analyses with readmission within 30 days (n = 8371) and 180 days (n = 7981) as the dependent variable were performed.

RESULTS

Older adult patients at nutritional risk during the index admission were 1.44 times more likely to be readmitted within 30 days after discharge (P < 0.001), and 1.47 times more likely to be readmitted within 180 days after discharge (P < 0.001), compared to older adult patients who were not at nutritional risk during index admission when adjusting for age, sex, discharge destination, diagnosis group, and length-of-stay.

CONCLUSIONS

Our results highlight the importance of focusing on nutritional status in older adults as a factor in the prevention of readmissions, including ensuring that practices, resources, and guidelines support appropriate screening procedures. Because nutritional risk predicts readmission both in a 30-days and 180-days perspective, the results point to the importance of ensuring follow-up on the screening result, both in the hospital context and after discharge.

Spanish version of the self-care self-efficacy scale: A validation study in community-dwelling older adults with chronic multimorbidity

OBJECTIVE

To test the psychometric properties of the Spanish version of the Self-Care Self-Efficacy Scale (SCSES-Sp) in community-dwelling older adults with chronic multimorbidity.

METHODS

A sample of 1013 community-dwelling older adults with chronic multimorbidity participated in an observational cross-sectional study that was carried out in 3 phases.

RESULTS

Confirmatory factor analysis showed that the SCSES-Sp has 4 dimensions: "self-efficacy in self-care behaviours based on clinical knowledge", "self-efficacy in self-care maintenance", "self-efficacy in self-care monitoring", and "self-efficacy in self-care management". A panel of independent experts considered the content of the SCSES-Sp valid. Convergent validity analysis showed moderate-strong correlations between all of the SCSES-Sp's dimensions and the reference criteria chosen. Reliability was good for the SCSES-Sp and all its dimensions. Test-retest reliability analysis showed that the SCSES-Sp was temporally stable.

CONCLUSIONS

The SCSES-Sp is a valid and reliable tool to assess self-efficacy in self-care in Spanish-speaking, community-dwelling older adults with chronic multimorbidity.

Risk Factors for Early Hospital Readmission in Geriatric Patients: A Systematic Review

The number of older patients is constantly growing, and early hospital readmissions in this population represent a major problem from a health, social and economic point of view. Furthermore, the early readmission rate is often used as an indicator of the quality of care. We performed a systematic review of the literature to better understand the risk factors of early readmission (30 and 90 days) in the geriatric population and to update the existing evidence on this subject. The search was carried out on the MEDLINE, EMBASE and PsycINFO databases. Three independent reviewers assessed the potential inclusion of the studies, and then each study was independently assessed by two reviewers using Joanna Briggs Institute critical appraisal tools; any discrepancies were resolved by the third reviewer. Studies that included inpatients in surgical wards were excluded. Twenty-nine studies were included in the review. Risk factors of early readmission can be classified into socio-economic factors, factors relating to the patient's health characteristics, factors related to the use of the healthcare system and clinical factors. Among these risk factors, those linked to patient frailty play an important role, in particular malnutrition, reduced mobility, risk of falls, fatigue and functional dependence. The early identification of patients at higher risk of early readmission may allow for targeted interventions in view of discharge.

Predictors of Functional Improvement, Length of Stay, and Discharge Destination in the Context of an Assess and Restore Program in Hospitalized Older Adults

Assess and restore programs such as Humber’s Elderly Assess and Restore Team (HEART) provide short-term restorative care to prevent functional decline in hospitalized older adults. The aim of this retrospective observational study was to determine which HEART participant characteristics are predictive of functional improvement, decreased length of stay, return to home, and decreased readmission to hospital. Electronic health records were retrospectively examined to gather predictor data. Differences in functional status, excessive length of stay, discharge destination, and hospital readmissions were compared in 547 HEART patients and 547 matched eligible non-participants using ANOVAs, Mann–Whitney, and chi-square tests. The greatest functional improvements (percent Barthel change) were seen in those requiring a one-person assist (M = 39.56) and using a walker (M = 46.07). Difference in excessive length of stay between HEART and non-HEART participants was greatest in those who used a walker (Mdn = 3.80), required a one-person assist (Mdn = 2.00), had a high falls risk (Mdn = 1.80), and had either a lower urinary tract infection (Mdn = 2.25) or pneumonia (Mdn = 1.70). Predictor variables did not affect readmission to the hospital nor return to home. Predictive characteristics should be considered when enrolling patients to assess and restore programs for optimal clinical outcomes.

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

BACKGROUND

Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance.

OBJECTIVE

We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system.

MATERIALS & METHODS

We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance.

RESULTS

Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85.

DISCUSSION

We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.

Supporting at-risk older adults transitioning from hospital to home: who benefits from an evidence-based patient-centered discharge planning intervention? Post-hoc analysis from a randomized trial

BACKGROUND

Subgroups of older patients experience difficulty performing activities of daily living (ADL) following hospital discharge, as well as unplanned hospital readmissions and emergency department (ED) presentations. We examine whether these subgroups of "at-risk" older patients benefit more than their counterparts from an evidence-based discharge planning intervention, on the following outcomes: (1) independence in ADL, (2) participation in life roles, (3) unplanned re-hospitalizations, and (4) ED presentations.

TRIAL DESIGN AND METHODS

This study used data from a randomized control trial involving 400 hospitalized older patients with acute and medical conditions, recruited through 5 sites in Australia. Participants receive either HOME, a patient-centered discharge planning intervention led by an occupational therapist; or a structured in-hospital consultation. HOME uses a collaborative approach for goal setting and includes pre and post-discharge home visits as well as telephone follow-up. Characteristics associated with higher risks of adverse outcomes were recorded and at-risk subgroups were created (mild cognitive impairment, walking difficulty, comorbidity, living alone and no support from family). Independence in ADL and participation in life roles were assessed with validated questionnaires. The number of unplanned re-hospitalizations and ED presentations were extracted from medical files. Linear regression models were conducted to detect variation in response to the intervention at 3-months, according to patients' characteristics.

RESULTS

Analyses revealed significant interaction effects for intervention by cognitive status for unplanned re-hospitalization (p = 0.003) and ED presentations (p = 0.021) at 3 months. Within the at-risk subgroup of mild cognitively impaired, the HOME intervention significantly reduced unplanned hospitalizations (p = 0.027), but the effect did not reach significance in ED visits. While the effect of HOME differed according to support received from family for participation in life roles (p = 0.019), the participation observed in HOME patients with no support was not significantly improved.

CONCLUSIONS

Findings show that hospitalized older adults with mild cognitive impairment benefit from the HOME intervention, which involves preparation and post-discharge support in the environment, to reduce unplanned re-hospitalizations. Improved discharge outcomes in this at-risk subgroup following an occupational therapist-led intervention may enable best care delivery as patients transition from hospital to home.

TRIAL REGISTRATION

The trial was registered before commencement (ACTRN12611000615987).