Dataset Information

Improving patient discharge and reducing hospital readmissions by using Intervention Mapping.

ABSTRACT:

Background

There is a growing impetus to reorganize the hospital discharge process to reduce avoidable readmissions and costs. The aim of this study was to provide insight into hospital discharge problems and underlying causes, and to give an overview of solutions that guide providers and policy-makers in improving hospital discharge.

Methods

The Intervention Mapping framework was used. First, a problem analysis studying the scale, causes, and consequences of ineffective hospital discharge was carried out. The analysis was based on primary data from 26 focus group interviews and 321 individual interviews with patients and relatives, and involved hospital and community care providers. Second, improvements in terms of intervention outcomes, performance objectives and change objectives were specified. Third, 220 experts were consulted and a systematic review of effective discharge interventions was carried out to select theory-based methods and practical strategies required to achieve change and better performance.

Results

Ineffective discharge is related to factors at the level of the individual care provider, the patient, the relationship between providers, and the organisational and technical support for care providers. Providers can reduce hospital readmission rates and adverse events by focusing on high-quality discharge information, well-coordinated care, and direct and timely communication with their counterpart colleagues. Patients, or their carers, should participate in the discharge process and be well aware of their health status and treatment. Assessment by hospital care providers whether discharge information is accurate and understood by patients and their community counterparts, are important examples of overcoming identified barriers to effective discharge. Discharge templates, medication reconciliation, a liaison nurse or pharmacist, regular site visits and teach-back are identified as effective and promising strategies to achieve the desired behavioural and environmental change.

Conclusions

This study provides a comprehensive guiding framework for providers and policy-makers to improve patient handover from hospital to primary care.

SUBMITTER: Hesselink G

PROVIDER: S-EPMC4175223 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Publications

Improving patient discharge and reducing hospital readmissions by using Intervention Mapping.

Hesselink Gijs G Zegers Marieke M Vernooij-Dassen Myrra M Barach Paul P Kalkman Cor C Flink Maria M Öhlen Gunnar G Olsson Mariann M Bergenbrant Susanne S Orrego Carola C Suñol Rosa R Toccafondi Giulio G Venneri Francesco F Dudzik-Urbaniak Ewa E Kutryba Basia B Schoonhoven Lisette L Wollersheim Hub H

BMC health services research 20140913

<h4>Background</h4>There is a growing impetus to reorganize the hospital discharge process to reduce avoidable readmissions and costs. The aim of this study was to provide insight into hospital discharge problems and underlying causes, and to give an overview of solutions that guide providers and policy-makers in improving hospital discharge.<h4>Methods</h4>The Intervention Mapping framework was used. First, a problem analysis studying the scale, causes, and consequences of ineffective hospital ...[more]

PMID: 25218406

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Project description:IntroductionNew financial incentives, such as reduced Medicare reimbursements, have led hospitals to closely monitor their readmission rates and initiate efforts aimed at reducing them. In this context, many surgical departments participate in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), which collects detailed demographic, laboratory, clinical, procedure and perioperative occurrence data. The availability of such data enables the development of data science methods which predict readmissions and, as done in this paper, offer specific recommendations aimed at preventing readmissions.Materials and methodsThis study leverages NSQIP data for 722,101 surgeries to develop predictive and prescriptive models, predicting readmissions and offering real-time, personalized treatment recommendations for surgical patients during their hospital stay, aimed at reducing the risk of a 30-day readmission. We applied a variety of classification methods to predict 30-day readmissions and developed two prescriptive methods to recommend pre-operative blood transfusions to increase the patient's hematocrit with the objective of preventing readmissions. The effect of these interventions was evaluated using several predictive models.ResultsPredictions of 30-day readmissions based on the entire collection of NSQIP variables achieve an out-of-sample accuracy of 87% (Area Under the Curve-AUC). Predictions based only on pre-operative variables have an accuracy of 74% AUC, out-of-sample. Personalized interventions, in the form of pre-operative blood transfusions identified by the prescriptive methods, reduce readmissions by 12%, on average, for patients considered as candidates for pre-operative transfusion (pre-operative hematoctic <30). The prediction accuracy of the proposed models exceeds results in the literature.ConclusionsThis study is among the first to develop a methodology for making specific, data-driven, personalized treatment recommendations to reduce the 30-day readmission rate. The reported predicted reduction in readmissions can lead to more than $20 million in savings in the U.S. annually.

Project description:ObjectiveTo determine whether patients discharged from hospital during the December holiday period have fewer outpatient follow-ups and higher rates of death or readmission than patients discharged at other times.DesignPopulation based retrospective cohort study.SettingAcute care hospitals in Ontario, Canada, 1 April 2002 to 31 January 2016.Participants217 305 children and adults discharged home after an urgent admission, during the two week December holiday period, compared with 453 641 children and adults discharged during two control periods in late November and January.Main outcome measuresThe primary outcome was death or readmission, defined as a visit to an emergency department or urgent rehospitalisation, within 30 days. Secondary outcomes were death or readmission and outpatient follow-up with a physician within seven and 14 days after discharge. Multivariable logistic regression with generalised estimating equations was used to adjust for characteristics of patients, admissions, and hospital.Results217 305 (32.4%) patients discharged during the holiday period and 453 641 (67.6%) discharged during control periods had similar baseline characteristics and previous healthcare utilisation. Patients who were discharged during the holiday period were less likely to have follow-up with a physician within seven days (36.3% v 47.8%, adjusted odds ratio 0.61, 95% confidence interval 0.60 to 0.62) and 14 days (59.5% v 68.7%, 0.65, 0.64 to 0.66) after discharge. Patients discharged during the holiday period were also at higher risk of 30 day death or readmission (25.9% v 24.7%, 1.09, 1.07 to 1.10). This relative increase was also seen at seven days (13.2% v 11.7%, 1.16, 1.14 to 1.18) and 14 days (18.6% v 17.0%, 1.14, 1.12 to 1.15). Per 100 000 patients, there were 2999 fewer follow-up appointments within 14 days, 26 excess deaths, 188 excess hospital admissions, and 483 excess emergency department visits attributable to hospital discharge during the holiday period.ConclusionsPatients discharged from hospital during the December holiday period are less likely to have prompt outpatient follow-up and are at higher risk of death or readmission within 30 days.

Project description:BackgroundTechnology is increasingly transforming the way we interact with others and undertake activities in our daily lives. The healthcare setting has, however, not yet realised the potential of technology solutions to facilitate communication between patients and healthcare providers. While the procedural and policy requirements of healthcare systems will ultimately drive such solutions, understanding the preferences and attitudes of patients is essential to ensure that technology implemented in the healthcare setting facilitates communication in safe, acceptable, and appropriate ways. Therefore, the purpose of this study was to examine patient preferences for using technology to communicate with health service providers about symptoms experienced following discharge from the hospital.MethodsPrimary data were collected from patients admitted to a large metropolitan hospital in Australia during three consecutive months in 2018. Participants were asked about their daily use of technology including use of computers, email, phone, text messaging, mobile applications, social media, online discussion forums, and videoconference. They were then asked about their use of technologies in managing their health, and preferences for use when communicating about symptoms with health service providers following discharge from hospital.ResultsFive hundred and twenty-five patients with a wide range of differing clinical conditions and demographics participated. Patients indicated they used a range of technologies in their everyday lives and to manage their health. Almost 60% of patients would prefer to return to hospital if they were experiencing symptoms of concern. However, if patients experienced symptoms that were not of concern, over 60% would prefer to communicate with the hospital via telephone or using technology. Admitting condition, income, and age were significantly associated with preferences for communication about symptoms following hospital discharge.ConclusionsPatients have varied preferences for communicating with their health service providers post-hospital discharge. Findings suggest that some, but not all patients, would prefer to use technology to traditional methods of communicating with the healthcare team. Health services should offer patients multiple options for communicating about their recovery to ensure individual needs are appropriately met.

Project description:BACKGROUND:Evidence-based interventions to reduce hospital readmissions may not generalize to resource-constrained safety-net hospitals. OBJECTIVE:To determine if an intervention by patient navigators (PNs), hospital-based Community Health Workers, reduces readmissions among high risk, low socioeconomic status patients. DESIGN:Randomized controlled trial. PARTICIPANTS:General medicine inpatients having at least one of the following readmission risk factors: (1) age??60 years, (2) any in-network inpatient admission within the past 6 months, (3) length of stay??3 days, (4) admission diagnosis of heart failure, or (5) chronic obstructive pulmonary disease. The analytic sample included 585 intervention patients and 925 controls. INTERVENTIONS:PNs provided coaching and assistance in navigating the transition from hospital to home through hospital visits and weekly telephone outreach, supporting patients for 30 days post-discharge with discharge preparation, medication management, scheduling of follow-up appointments, communication with primary care, and symptom management. MAIN MEASURES:The primary outcome was in-network 30-day hospital readmissions. Secondary outcomes included rates of outpatient follow-up. We evaluated outcomes for the entire cohort and stratified by patient age >60 years (425 intervention/584 controls) and ?60 years (160 intervention/341 controls). KEY RESULTS:Overall, 30-day readmission rates did not differ between intervention and control patients. However, the two age groups demonstrated marked differences. Intervention patients >60 years showed a statistically significant adjusted absolute 4.1% decrease [95% CI: -8.0%, -0.2%] in readmission with an increase in 30-day outpatient follow-up. Intervention patients ?60 years showed a statistically significant adjusted absolute 11.8% increase [95% CI: 4.4%, 19.0%] in readmission with no change in 30-day outpatient follow-up. CONCLUSIONS:A patient navigator intervention among high risk, safety-net patients decreased readmission among older patients while increasing readmissions among younger patients. Care transition strategies should be evaluated among diverse populations, and younger high risk patients may require novel strategies.

Dataset Information

Improving patient discharge and reducing hospital readmissions by using Intervention Mapping.

Background

Methods

Results

Conclusions

Publications

Improving patient discharge and reducing hospital readmissions by using Intervention Mapping.

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