Predicting Potentially Avoidable Hospitalizations

Gao, Jian; Moran, Eileen; Li, Yufang; Almenoff, Peter L.

doi:10.1097/mlr.0000000000000041

Cited by 58 publications

(68 citation statements)

References 32 publications

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“…The majority of models (18 of 27) were developed to predict emergency hospital admission at 12-month follow-up (range, 90 d–4 y). Of these, 3 models focused on emergency admissions for chronic disease or conditions amenable to primary care management as a primary outcome measure 27,31,38. Two models predicted any hospitalization and 2 predicted occupied bed days over specific time periods 17,26,32,38.…”

Section: Resultsmentioning

confidence: 99%

“…Seven studies presented their final risk model only and not all variables considered for inclusion, and 1 study uses locally available data to create a risk prediction model specifically for a named population so variables considered for inclusion vary 23–25,27,28,31,34,37. The most frequently included predictor variables in final risk models were: (1) named medical diagnoses (23 models); (2) age (23 models); (3) prior emergency admission (22 models); and (4) sex (18 models).…”

Section: Resultsmentioning

confidence: 99%

“…Polypharmacy was considered as a predictor variable in 14 models and included in 11 final models 11,18,19,21,23,24,28–30,37,39. Five models included a specific measure of socioeconomic group (SEG) and a further 3 used either employment history or income as proxy measures for SEG 17,21–23,25,28,29,31…”

Section: Resultsmentioning

confidence: 99%

“…Overall, a smaller number of models (n=11) included nonmedical factors 11,13–15,17,20–22,24,31,37. These variables were largely included in self-report data models (Table 1).…”

Section: Resultsmentioning

confidence: 99%

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Risk Prediction Models to Predict Emergency Hospital Admission in Community-dwelling Adults

et al. 2014

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Overall, a smaller number of models (n=11) included nonmedical factors 11,13–15,17,20–22,24,31,37. These variables were largely included in self-report data models (Table 1).…”

Section: Resultsmentioning

confidence: 99%

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Risk Prediction Models to Predict Emergency Hospital Admission in Community-dwelling Adults

et al. 2014

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“…67 Most models, until more recently, did not include mental health data. A recent systematic review of risk prediction models to predict EHA in community-dwelling adults identified 27 unique risk prediction models, 349 of which only 12 included data on mental illness, 67,[350][351][352][353][354][355][356][357][358] three included data on living alone 356,359,360 and none included data about threatening life events. Of the 12 models that included data on mental illness, all were published during the lifetime of this programme.…”

Section: Relevance Of Programme Findings For Risk Modellingmentioning

confidence: 99%

CHOICE: Choosing Health Options In Chronic Care Emergencies

Guthrie

Afzal

Blakeley

et al. 2017

Programme Grants Appl Res

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BackgroundOver 70% of the health-care budget in England is spent on the care of people with long-term conditions (LTCs), and a major cost component is unscheduled health care. Psychological morbidity is high in people with LTCs and is associated with a range of adverse outcomes, including increased mortality, poorer physical health outcomes, increased health costs and service utilisation.ObjectivesThe aim of this programme of research was to examine the relationship between psychological morbidity and use of unscheduled care in people with LTCs, and to develop a psychosocial intervention that would have the potential to reduce unscheduled care use. We focused largely on emergency hospital admissions (EHAs) and attendances at emergency departments (EDs).DesignA three-phase mixed-methods study. Research methods included systematic reviews; a longitudinal prospective cohort study in primary care to identify people with LTCs at risk of EHA or ED admission; a replication study in primary care using routinely collected data; an exploratory and feasibility cluster randomised controlled trial in primary care; and qualitative studies to identify personal reasons for the use of unscheduled care and factors in routine consultations in primary care that may influence health-care use. People with lived experience of LTCs worked closely with the research team.SettingPrimary care. Manchester and London.ParticipantsPeople aged ≥ 18 years with at least one of four common LTCs: asthma, coronary heart disease, chronic obstructive pulmonary disease (COPD) and diabetes. Participants also included health-care staff.ResultsEvidence synthesis suggested that depression, but not anxiety, is a predictor of use of unscheduled care in patients with LTCs, and low-intensity complex interventions reduce unscheduled care use in people with asthma and COPD. The results of the prospective study were that depression, not having a partner and life stressors, in addition to prior use of unscheduled care, severity of illness and multimorbidity, were independent predictors of EHA and ED admission. Approximately half of the cost of health care for people with LTCs was accounted for by use of unscheduled care. The results of the replication study, carried out in London, broadly supported our findings for risk of ED attendances, but not EHAs. This was most likely due to low rates of detection of depression in general practitioner (GP) data sets. Qualitative work showed that patients were reluctant to use unscheduled care, deciding to do so when they perceived a serious and urgent need for care, and following previous experience that unscheduled care had successfully and unquestioningly met similar needs in the past. In general, emergency and primary care doctors did not regard unscheduled care as problematic. We found there are missed opportunities to identify and discuss psychosocial issues during routine consultations in primary care due to the ‘overmechanisation’ of routine health-care reviews. The feasibility trial examined two levels of an intervention for people with COPD: we tried to improve the way in which practices manage patients with COPD and developed a targeted psychosocial treatment for patients at risk of using unscheduled care. The former had low acceptability, whereas the latter had high acceptability. Exploratory health economic analyses suggested that the practice-level intervention would be unlikely to be cost-effective, limiting the value of detailed health economic modelling.LimitationsThe findings of this programme may not apply to all people with LTCs. It was conducted in an area of high social deprivation, which may limit the generalisability to more affluent areas. The response rate to the prospective longitudinal study was low. The feasibility trial focused solely on people with COPD.ConclusionsPrior use of unscheduled care is the most powerful predictor of unscheduled care use in people with LTCs. However, psychosocial factors, particularly depression, are important additional predictors of use of unscheduled care in patients with LTCs, independent of severity and multimorbidity. Patients and health-care practitioners are unaware that psychosocial factors influence health-care use, and such factors are rarely acknowledged or addressed in consultations or discussions about use of unscheduled care. A targeted patient intervention for people with LTCs and comorbid depression has shown high levels of acceptability when delivered in a primary care context. An intervention at the level of the GP practice showed little evidence of acceptability or cost-effectiveness.Future workThe potential benefits of case-finding for depression in patients with LTCs in primary care need to be evaluated, in addition to further evaluation of the targeted patient intervention.FundingThe National Institute for Health Research Programme Grants for Applied Research programme.

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Trends in Hospitalization vs Observation Stay for Ambulatory Care–Sensitive Conditions

et al. 2019

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Predicting Potentially Avoidable Hospitalizations

Abstract: Our analyses demonstrate that administrative data can be effective in predicting ACSC hospitalizations. With high predictive ability, the model can assist primary care providers to identify high-risk patients for early intervention to reduce ACSC hospitalizations.

Cited by 58 publications

References 32 publications

Risk Prediction Models to Predict Emergency Hospital Admission in Community-dwelling Adults

Risk Prediction Models to Predict Emergency Hospital Admission in Community-dwelling Adults

CHOICE: Choosing Health Options In Chronic Care Emergencies

Trends in Hospitalization vs Observation Stay for Ambulatory Care–Sensitive Conditions

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