Networks of hospital discharge planning teams and readmissions

The value that social workers' competencies add to health care: An integrative review

Health and social care professionals' competencies have traditionally been separated because of the different aims of the two professions. These competencies need to be integrated, to make sure that seamless services are provided that meet the often complex needs of patients and clients in a coordinated and timely way. The aim of this integrative review was to identify, describe and synthetise previous studies on integrated competencies in health and social care. Electronic literature searches were carried out on the CINAHL, ProQuest, PsycInfo, PubMed, Scopus and SocIndex databases for peer-reviewed scientific papers that were published in English between 1 January 2007 and 31 December 2019. This identified 3,231 papers, after duplicates were removed, and 18 focused on the integration of social workers' competencies with health care. Other types of integration were not found. The value added by integrating social workers' competencies with health care focused on engaging working orientation, improving communication with family members, increasing understanding of service resources and mastering successful discharge procedures so that they met comprehensive, complex health and well-being needs. Social workers added value when they worked with multi-professional teams, but there were challenges to integrating competencies and these were related to professional collaboration and fragmented leadership. In future, more attention needs to be paid to diversifying and optimising the integration of professional health and social care competencies that meet clients' and patients' care and service needs. It is also vital to focus on developing the professional and leadership strategies that are needed to combine those competencies.

Systematic review of prediction models for postacute care destination decision-making

OBJECTIVE

This article reports a systematic review of studies containing development and validation of models predicting postacute care destination after adult inpatient hospitalization, summarizes clinical populations and variables, evaluates model performance, assesses risk of bias and applicability, and makes recommendations to reduce bias in future models.

MATERIALS AND METHODS

A systematic literature review was conducted following PRISMA guidelines and the Cochrane Prognosis Methods Group criteria. Online databases were searched in June 2020 to identify all published studies in this area. Data were extracted based on the CHARMS checklist, and studies were evaluated based on predictor variables, validation, performance in validation, risk of bias, and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool.

RESULTS

The final sample contained 28 articles with 35 models for evaluation. Models focused on surgical (22), medical (5), or both (8) populations. Eighteen models were internally validated, 10 were externally validated, and 7 models underwent both types. Model performance varied within and across populations. Most models used retrospective data, the median number of predictors was 8.5, and most models demonstrated risk of bias.

DISCUSSION AND CONCLUSION

Prediction modeling studies for postacute care destinations are becoming more prolific in the literature, but model development and validation strategies are inconsistent, and performance is variable. Most models are developed using regression, but machine learning methods are increasing in frequency. Future studies should ensure the rigorous variable selection and follow TRIPOD guidelines. Only 14% of the models have been tested or implemented beyond original studies, so translation into practice requires further investigation.

The effect of discharge planning on length of stay and readmission rates of older adults in acute hospitals: A systematic review and meta-analysis of systematic reviews

AIM

To examine the effectiveness of discharge planning on length of stay and readmission rates among older adults in acute hospitals.

BACKGROUND

Discharge planning takes place in all acute hospital settings in many forms. However, it is unclear how it contributes to reducing patient length of stay in hospital and readmission rates.

METHODS

Seven systematic reviews were identified and examined. All of the systematic reviews explored the impact of discharge planning on length of stay and readmission rates.

RESULTS

A limited meta-analysis of the results in relation to length of stay indicates positive finding for discharge planning as an intervention (MD = -0.71(95% CI -1.05,-0.37; p = .0001)). However, further analysis of the broader findings in relation to length of stay indicates inconclusive or mixed results. In relation to readmission rates both meta-analysis and narrative analysis point to a reduced risk for older people where discharge planning has taken place (RR = 0.78 (95% CI: 0.72, 0.84; p = .00001)). The ability to synthesize results however is severely hampered by the diversity of approaches to research in this area.

IMPLICATIONS FOR NURSING MANAGEMENT

It is unclear what impact discharge planning has on length of stay of older people. Indeed, while nurse mangers will be interested in gauging this impact on throughput and patient flow, it is questionable if length of stay is the correct outcome to measure when studying discharge planning as good discharge planning may increase length of stay. Readmission rates may be a more appropriate outcome measure but standardization of approach needs to be considered in this regard. This would assist nurse managers in assessing the impact of discharge planning processes.

Ready to Go Home? Assessment of Shared Mental Models of the Patient and Discharging Team Regarding Readiness for Hospital Discharge

BACKGROUND

A critical task of the inpatient interprofessional team is readying patients for discharge. Assessment of shared mental model (SMM) convergence can determine how much team members agree about patient discharge readiness and how their mental models align with the patient's self-assessment.

OBJECTIVE

Determine the convergence of interprofessional team SMMs of hospital discharge readiness and identify factors associated with these assessments.

DESIGN

We surveyed interprofessional discharging teams and each team's patient at time of hospital discharge using validated tools to capture their SMMs.

PARTICIPANTS

Discharge events (N = 64) from a single hospital consisting of the patient and their team (nurse, coordinator, physician).

MEASURES

Clinician and patient versions of the validated Readiness for Hospital Discharge Scales/Short Form (RHDS/SF). We measured team convergence by comparing the individual clinicians' scores on the RHDS/SF, and we measured team-patient convergence as the absolute difference between the Patient-RHDS/SF score and the team average score on the Clinician-RHDS/SF.

RESULTS

Discharging teams assessed patients as having high readiness for hospital discharge (mean score, 8.5 out of 10; SD, 0.91). The majority of teams had convergent SMMs with high to very high interrater agreement on discharge readiness (mean r*wg(J), 0.90; SD, 0.10). However, team-patient SMM convergence was low: Teams overestimated the patient's self-assessment of readiness for discharge in 48.4% of events. We found that teams reporting higher-quality teamwork (P = .004) and bachelor's level-trained nurses (P < .001) had more convergent SMMs with the patient.

CONCLUSION

Measuring discharge teams' SMM of patient discharge readiness may represent an innovative assessment tool and potential lever to improve the quality of care transitions.

Who is Responsible for Discharge Education of Patients? A Multi-Institutional Survey of Internal Medicine Residents

BACKGROUND

Safely and effectively discharging a patient from the hospital requires working within a multidisciplinary team. However, little is known about how perceptions of responsibility among the team impact discharge communication practices.

OBJECTIVE

Our study attempts to understand residents' perceptions of who is primarily responsible for discharge education, how these perceptions affect their own reported communication with patients, and how residents envision improving multidisciplinary communication around discharges.

DESIGN

A multi-institutional cross-sectional survey.

PARTICIPANTS

Internal medicine (IM) residents from seven US residency programs at academic medical centers were invited to participate between March and May 2019, via email of an electronic link to the survey.

MAIN MEASURES

Data collected included resident perception of who on the multidisciplinary team is primarily responsible for discharge communication, their own reported discharge communication practices, and open-ended comments on ways discharge multidisciplinary team communication could be improved.

KEY RESULTS

Of the 613 resident responses (63% response rate), 35% reported they were unsure which member of the multidisciplinary team is primarily responsible for discharge education. Residents who believed it was either the intern's or the resident's primary responsibility had 4.28 (95% CI, 2.51-7.30) and 3.01 (95% CI, 1.66-5.71) times the odds, respectively, of reporting doing discharge communication practices frequently compared to those who were not sure who was primarily responsible. To improve multidisciplinary discharge communication, residents called for the following among team members: (1) clarifying roles and responsibilities for communication with patients, (2) setting expectations for communication among multidisciplinary team members, and (3) redefining culture around discharges.

CONCLUSIONS

Residents report a lack of understanding of who is responsible for discharge education. This diffusion of ownership impacts how much residents invest in patient education, with more perceived responsibility associated with more frequent discharge communication.

A mixed methods study examining teamwork shared mental models of interprofessional teams during hospital discharge

BACKGROUND

Little is known about how team processes impact providers' abilities to prepare patients for a safe hospital discharge. Teamwork Shared Mental Models (teamwork-SMMs) are the teams' organised understanding of individual member's roles, interactions and behaviours needed to perform a task like hospital discharge. Teamwork-SMMs are linked to team effectiveness in other fields, but have not been readily investigated in healthcare. This study examines teamwork-SMMs to understand how interprofessional teams coordinate care when discharging patients.

METHODS

This mixed methods study examined teamwork-SMMs of inpatient interprofessional discharge teams at a single hospital. For each discharge event, we collected data from the patient and their discharge team (nurse, physician and coordinator) using interviews and questionnaires. We quantitatively determined the discharge teams' teamwork-SMM components of quality and convergence using the Shared Mental Model Scale, and then explored their relationships to patient-reported preparation for posthospital care. We used qualitative thematic analysis of narrative cases to examine the contextual differences of discharge teams with higher versus lower teamwork-SMMs.

RESULTS

The sample included a total of 106 structured patient interviews, 192 provider day-of-discharge questionnaires and 430 observation hours to examine 64 discharge events. We found that inpatient teams with better teamwork-SMMs (ie, higher perceptions of teamwork quality or greater convergence) were more effective at preparing patients for post-hospital care. Additionally, teams with high and low teamwork-SMMs had different experiences with team cohesion, communication openness and alignment on the patient situation.

CONCLUSIONS

Examining the quality and agreement of teamwork-SMMs among teams provides a better understanding of how teams coordinate care and may facilitate the development of specific team-based interventions to improve patient care at hospital discharge.

A Network Approach to Stroke Systems of Care

The landscape of stroke systems of care is evolving as patients are increasingly transferred between hospitals for access to higher levels of care. This is driven by time-sensitive disability-reducing interventions such as mechanical thrombectomy. However, coordination and triage of patients for such treatment remain a challenge worldwide, particularly given complex eligibility criteria and varying time windows for treatment. Network analysis is an approach that may be applied to this problem. Hospital networks interlinked by patients moved from facility to facility can be studied using network modeling that respects the interdependent nature of the system. This allows understanding of the central hubs, the change of network structure over time, and the diffusion of innovations. This topical review introduces the basic principles of network science and provides an overview on the applications and potential interventions in stroke systems of care.