Health Outcomes from Home Hospitalization: Multisource Predictive Modeling

Calvo, Mireia; González, Rubén; Seijas, Núria; Vela, Emili; Hernández, Carme; Batiste, Guillem; Miralles, Felip; Roca, Josep; Cano, Isaac; Jané, Raimon

doi:10.2196/21367

Cited by 17 publications

(26 citation statements)

References 31 publications

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“…In line with the existing literature, home hospitalization and territorial medicine are positively related to efficiency in both models [ 60 ]. Both are measures to strengthen peripheral units, that are endowed with a high degree of autonomy.…”

Section: Discussionsupporting

confidence: 83%

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The other side of the crisis: organizational flexibility in balancing Covid-19 and non-Covid-19 health-care services

Troisi

Simone

Vargas

et al. 2022

BMC Health Serv Res

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Background Many healthcare systems have been unable to deal with Covid-19 without influencing non-Covid-19 patients with pre-existing conditions, risking a paralysis in the medium term. This study explores the effects of organizational flexibility on hospital efficiency in terms of the capacity to deliver healthcare services for both Covid-19 and non-Covid-19 patients. Method Focusing on Italian health system, a two-step strategy is adopted. First, Data Envelope Analysis is used to assess the capacity of hospitals to address the needs of Covid-19 and non-Covid-19 patients relying on internal resource flexibility. Second, two panel regressions are performed to assess external organizational flexibility, with the involvement in demand management of external operators in the health-care service, examining the impact on efficiency in hospital capacity management. Results The overall response of the hospitals in the study was not fully effective in balancing the needs of the two categories of patients (the efficiency score is 0.87 and 0.58, respectively, for Covid-19 and non-Covid-19 patients), though responses improved over time. Furthermore, among the measures providing complementary services in the community, home hospitalization and territorial medicine were found to be positively associated with hospital efficiency (0.1290, p < 0.05 and 0.2985, p < 0.01, respectively, for non-Covid-19 and Covid-19 patients; 0.0026, p < 0.05 and 0.0069, p < 0.01, respectively, for non-Covid-19 and Covid-19). In contrast, hospital networks are negatively related to efficiency in Covid-19 patients (-0.1037, p < 0.05), while the relationship is not significant in non-Covid-19 patients. Conclusions Managing the needs of Covid-19 patients while also caring for other patients requires a response from the entire healthcare system. Our findings could have two important implications for effectively managing health-care demand during and after the Covid-19 pandemic. First, as a result of a naturally progressive learning process, the resource balance between Covid-19 and non-Covid-19 patients improves over time. Second, it appears that demand management to control the flow of patients necessitates targeted interventions that combine agile structures with decentralization. Finally, untested integration models risk slowing down the response, giving rise to significant costs without producing effective results.

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Section: Discussionsupporting

confidence: 83%

“…Home hospitalization is a cost-effective alternative to conventional hospitalization for selected patients. In addition, some studies have shown a positive impact on quality of care, and patient satisfaction [ 60 ]. It is measured here as the percentage of special units established in relation to those expected for the population.…”

Section: Methodsmentioning

confidence: 99%

The other side of the crisis: organizational flexibility in balancing Covid-19 and non-Covid-19 health-care services

Troisi

Simone

Vargas

et al. 2022

BMC Health Serv Res

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“…In our center, a tertiary university hospital providing specialized care to a catchment population of 520,000 citizens, HaH was implemented in 2006 as a mainstream service across specialties covering two modalities of HaH: hospital avoidance (HaH-HA) and early discharge (HaH-ED) [7,15,16]. The service provides acute, home-based, short-term care aiming at either entirely replacing conventional hospitalization (hospital avoidance) or accelerating discharge (early discharge).…”

Section: Introductionmentioning

confidence: 99%

The Value of Admission Avoidance: Cost-Consequence Analysis of One-Year Activity in a Consolidated Service

Hernández

Herranz

Baltaxe

et al. 2023

Preprint

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Background: Many advantages of hospital at home (HaH), as a modality of acute care, have been highlighted, but controversies exist regarding the cost-benefit trade-offs. Objective: To assess health outcomes and analytical costs of hospital avoidance (HaH-HA) in a consolidated service with over ten years of delivery of HaH in Barcelona (Spain). Methods: A retrospective cost-consequence analysis of all first episodes of HaH-HA, directly admitted from the emergency room (ER) between 2017 and 2018, was carried out. HaH-HA was compared with a propensity-score-matched group of contemporary patients admitted to conventional hospitalization (Controls). Mortality, re-admissions, ER visits, and direct healthcare costs were evaluated. Registration: ClinicalTrials.gov (26/04/2017;NCT03130283). Results: HaH-HA and Controls (n=441 each) were comparable in terms of age (73 [SD16] vs 74 [16]), gender (male, 57% vs 59%), multimorbidity, healthcare expenditure during the previous year, case mix index of the acute episode, and main diagnosis at discharge. HaH-HA presented lower mortality during the episode (0 vs. 19 (4.3%); p < 0.001). At 30 days post-discharge, HaH-HA and Controls showed similar re-admission rates; however, ER visits were lower in HaH-HA than in Controls (28 (6.3%) vs 34 (8.1%); p = 0.044). Average costs per patient during the episode were lower in the HaH-HA group (1,078 EUR) than in Controls (2,171 EUR). Likewise, healthcare costs within the 30 days post-discharge were also lower in HaH-Ha than in Controls (p < 0.001). Conclusions: The study showed higher performance and cost reductions of HaH-HA in a real-world setting. The identification of sources of savings facilitates scaling of hospital avoidance. Funding: This article was funded by JADECARE project- HP-JA-2019 - Grant Agreement number 951442 (2020-2023), a European Union Health Program 2014-2020. Keywords: Hospital at home; Early readmissions; Health Services Research; Implementation science; Transitional Care.

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“…In this regard, multisource clinical predictive modeling approaches, considering various determinants of health (eg, clinical, social, populational, lifestyle), have become an effective strategy for subject-specific risk assessment to prevent morbidity-related adverse events leading to hospital readmissions [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

González-Colom¹,

Herranz²,

Vela³

et al. 2023

J Med Internet Res

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Background Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. Objective The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients’ profiles for personalized service selection purposes. Methods Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. Results Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients’ profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). Conclusions The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

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Health Outcomes from Home Hospitalization: Multisource Predictive Modeling

Cited by 17 publications

References 31 publications

The other side of the crisis: organizational flexibility in balancing Covid-19 and non-Covid-19 health-care services

The other side of the crisis: organizational flexibility in balancing Covid-19 and non-Covid-19 health-care services

The Value of Admission Avoidance: Cost-Consequence Analysis of One-Year Activity in a Consolidated Service

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

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