Enhanced Discharge Planning Research Articles

The Montreal Cognitive Assessment detects cognitive deficits that go unnoticed during clinical observation in the acute phase after stroke

ABSTRACT Background Detecting cognitive impairments early after stroke is essential for appropriate referrals. Although recommended in stroke guidelines, early cognitive screening is not always implemented. We assessed whether the Montreal Cognitive Assessment (MoCA) adds diagnostic value compared to clinical observation alone. In addition, discharge destinations for stroke patients with and without cognitive deficits detected with the screening tool or the treatment team were explored. Methods Forty-four stroke patients were screened with the MoCA during stroke unit admission. Their charts were studied for cognitive impairments reported by the stroke care team, who were blinded to screening scores. Proportions of detected cognitive deficits were compared between screening (score <26) and patient charts. Discharge destination distribution (home vs. rehabilitation) was explored. Results The proportion of cognitively impaired patients indicated by the MoCA (84%) and reported in patients’ charts (25%) differed significantly (p < 0.001). The distribution of discharge destination did not suggest an association with the detection of cognitive deficits by the treatment team or the cognitive screening. Conclusions The MoCA detects more cognitive deficits than clinical impression alone, emphasizing the importance of standard screening for cognitive impairments in acute stroke patients. Ultimately, systematic screening may enhance discharge planning and improve long-term outcomes.

Physician Financial Incentives to Reduce Unplanned Hospital Readmissions: A Propensity Score Weighted Cohort Study

BackgroundUnplanned hospital readmissions are associated with adverse patient outcomes and substantial healthcare costs. It remains unknown whether physician financial incentives for enhanced discharge planning can reduce readmission risk. MethodsIn 2012, policymakers in British Columbia, Canada, introduced a $75 fee-for-service physician payment to incentivize enhanced discharge planning (the “G78717” fee code). We used population-based administrative health data to compare outcomes among G78717-exposed and G78717-unexposed patients. We identified all nonelective hospitalizations potentially eligible for the incentive over a 5-year study interval. We examined the composite risk of unplanned readmission or death and total direct healthcare costs accrued within 30 days of discharge. Propensity score overlap weights and adjustment were used to account for differences between exposed and unexposed patients. ResultsA total of 5262 of 24,787 G78717-exposed and 28,096 of 136,541 unexposed patients experienced subsequent unplanned readmission or death, suggesting exposure to the G78717 incentive did not reduce the risk of adverse outcomes after discharge (crude percent, 21.1% vs 20.6%; adjusted odds ratio, 0.97; 95% CI, 0.93-1.01; P = .23). Mean direct healthcare costs within 30 days of discharge were $3082 and $2993, respectively (adjusted cost ratio, 1.00; 95% CI, 0.95-1.05; P = .93). ConclusionsA physician financial incentive that encouraged enhanced hospital discharge planning did not reduced the risk of readmission or death, and did not significantly decrease direct healthcare costs. Policymakers should consider the baseline prevalence and effectiveness of enhanced discharge planning, the magnitude and design of financial incentives, and whether auditing of incentivized activities is required when implementing similar incentives elsewhere. Trial RegistrationClinicalTrials.gov ID, NCT03256734

Transitional Care Models for Stroke Survivors to Improve Quality of Care through Bridging the Care Gap from Hospital to Home: A State of Art Review

Introduction : Stroke is a life threatening medical emergency that occurs suddenly and impact greatly in all aspect of an individual’s normal life since the early stage to long term. Various types of transitional care models are using by Health systems to improve care transition among stroke patients. Aims:&#x0D; This paper aims to explore the existing transitional care models used to improve the quality of care and patient’s outcome among stroke survivors.&#x0D; Methods: An Integrated review was conducted of empirical literature available in PubMed, Google Scholar, ProQuest, and NepJol by June 20, 2022 to identify studies of hospital to home care transitions of stroke patients with quality of care as the primary outcome.&#x0D; Results: There are 6 main types of transitional care model which focused on care transition for stroke survivors: 1) Naylor’s Transitional Care Model (TCM), 2) Coleman’s Care Transition Interventions (CTI), 3) Project Re-engineered Discharge (project RED), 4) Better Outcomes by Optimizing Safe Transition (Project Boost), 5) Enhanced Discharge Planning Program and 6) Comprehensive Post- Acute Stroke Services (COMPASS). Although, all models are differing by design, each of the models can provide a framework for managing health conditions from hospital to home settings in collaboration with the client and his/her family. Hence, the transitional care Models are effective in bridging the care gap between hospital to home and improve the quality of health care and patient outcomes among stroke survivors.&#x0D;

Physician Financial Incentives to Reduce Unplanned Hospital Readmissions: an Interrupted Time Series Analysis.

In 2012, the Ministry of Health in British Columbia, Canada, introduced a $75 incentive payment that could be claimed by hospital physicians each time they produced a written post-discharge care plan for a complex patient at the time of hospital discharge. To examine whether physician financial payments incentivizing enhanced discharge planning reduce subsequent unplanned hospital readmissions. Interrupted time series analysis of population-based hospitalization data. Individuals with one or more eligible hospitalizations occurring in British Columbia between 2007 and 2017. The proportion of index hospital discharges with subsequent unplanned hospital readmission within 30 days, as measured each month of the 11-year study interval. We used interrupted time series analysis to determine if readmission risk changed after introduction of the incentive payment policy. A total of 40,588 unplanned hospital readmissions occurred among 409,289 eligible index hospitalizations (crude 30-day readmission risk, 9.92%). Policy introduction was not associated with a significant step change (0.393%; 95CI, - 0.190 to 0.975%; p = 0.182) or change-in-trend (p = 0.317) in monthly readmission risk. Policy introduction was associated with significantly fewer prescription fills for potentially inappropriate medications among older patients, but no improvement in prescription fills for beta-blockers after cardiovascular hospitalization and no change in 30-day mortality. Incentive payment uptake was incomplete, rising from 6.4 to 23.5% of eligible hospitalizations between the first and last year of the post-policy interval. The introduction of a physician incentive payment was not associated with meaningful changes in hospital readmission rate, perhaps in part because of incomplete uptake by physicians. Policymakers should consider these results when designing similar interventions elsewhere. ClinicalTrials.gov ID, NCT03256734.

Abstract 305: Characteristics of Heart Failure Patients Referred to the Bridge Transitional Care Clinic By Time to Readmission

Background: Readmission reduction initiatives emphasize prompt follow-up post-discharge. Identifying factors that influence early readmission may inform discharge planning. We compared characteristics of heart failure (HF) patients (pts) based on time to readmission to determine which pt characteristics were associated with early readmission. Methods: Pts referred to the BRIDGE clinic following index admission for HF from 2008-2017 were eligible. Demographics and inpatient clinical characteristics were compared between 1) pts who were and were not readmitted within 30 days post-index discharge, and 2) pts who were readmitted early (0-7 days post-discharge) versus late (8-30 days post-discharge). Results: Of 978 HF pts, 226 (23.1%) were readmitted within 30 days. Compared to those not readmitted, 30-day readmits were more likely to be male, white, and have higher NYHA class, longer index stay, ICU admission during index admission, and lower Hgb, higher Cr, and higher BUN during index admission. Among those with a 30 day readmit, 56 (24.8%) were readmitted within 7 days of discharge. Early readmits were more often female (p=0.07) and had index stays in the ICU (p=0.07). Conclusion: Pts readmitted within 30 days had more complicated hospital courses than those not readmitted, and those readmitted early had higher incidences of females and index stays in the ICU. Efforts to define a high risk subset of HF pts likely to be readmitted early and targeting them for enhanced discharge planning is warranted.

Causes of Cancer Re-Admissions: A Patient-Centered Approach.

Patients with a cancer diagnosis have a high risk of re-admission during the 30 days after discharge. Clinicians, payers, and patients consider this to be an indicator of care quality. The causes of re-admission remain poorly understood. Retrospective chart reviews, used in most re-admission research, provide limited information regarding the causes of, and methods to reduce, re-admissions. This project sought to elicit the insights of those most affected by re-admission: the patients themselves. We interviewed patients with cancer who were re-admitted to 2 urban teaching hospitals when they were hospitalized during their second admission. Trained interviewers used a semistructured interview guide to gather information on events just before the second admission, the patients' understanding of the cause of re-admission, and the patients' views about their readiness for discharge at the previous admission. Interviews were transcribed and analyzed, and themes were identified and explored. Three major themes were identified. First, most patients saw their re-admission as caused by problems that could not be treated in an outpatient setting. Second, the patients felt that they did not have sufficient resources at home to manage their care. Furthermore, the patients did not see the outpatient care team as a resource that they could call on for assistance. As a result, most of the decisions to return to the hospital were made by the patients themselves. The decision that leads to re-admission often happens at home, in response to unmanageable needs. Strengthening the bond between the care team and the patient, with the aim of providing care in the most appropriate setting, could decrease re-admissions in patients with cancer. Possible interventions include home visits, enhanced discharge planning, and telehealth services.

'Not safe for discharge'? Words, values, and person-centred care.

The phrase 'not safe for discharge home' is often heard in relation to an older person in hospital, commonly due to functional limitations or risk of falls. But it remains unclear how such a standard of safety should be set in this context, or who should set it. In addition, labelling someone 'unsafe' to return to their own home has significant practical and ethical implications. After briefly exploring these issues, this Commentary suggests that a holistic approach and shared decision-making is required in this setting. Instead of simply declaring someone safe or unsafe for discharge home, specific 'safety concerns' (or 'hazards') should be identified and addressed as able. Ongoing specific concerns can then be discussed in conjunction with a patient's values and perceived benefits of returning home, in comparison with potential pros and cons of other discharge options. Overall, this paper suggests that paying attention to our words and values can enhance discharge planning and person-centred care.

PREDICTING READMISSION RISK FOLLOWING PERCUTANEOUS CORONARY INTERVENTION AT THE TIME OF ADMISSION

Abstract Objective To investigate whether a prediction model based on data available early in percutaneous coronary intervention (PCI) admission can predict the risk of readmission. Background Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after PCI early in a hospitalization would enable hospitals to enhance discharge planning. Methods We developed 3 different models to predict 30-day inpatient readmission to our institution for patients who underwent PCI between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from CathPCI Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent PCI between May 2013 and September 2015. Results Our cohort included 6717 PCI patients; 3739 in the derivation cohort and 2978 in the validation cohort. The discriminative ability of the admission model was good (C-index of 0.727). The c-indices for the discharge and cath PCI models were slightly better. (C-index of 0.751 and 0.752 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.720, 0.739 and 0.741 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.703, 0.725 and 0.719 respectively). Conclusion Simple models based on available demographic and clinical data may be sufficient to identify patients at highest risk of readmission following PCI early in their hospitalization.

PREDICTING READMISSION RISK FOLLOWING CORONARY ARTERY BYPASS SURGERY AT THE TIME OF ADMISSION

Abstract Background Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning. Methods We developed different models to predict 30-day inpatient readmission to our institution in patients who underwent CABG between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from STS Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent CABG between May 2013 and September 2015. Our cohort included 1277 CABG patients: 1159 in the derivation cohort and 1018 in the validation cohort. Results The discriminative ability of the admission model was reasonable (C-index of 0.673). The c-indices for the discharge and STS models were slightly better. (C-index of 0.700 and 0.714 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.641, 0.659 and 0.670 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.573, 0.605 and 0.595 respectively). Conclusions Risk prediction models based on data available early on admission are predictive for readmission risk. Adding registry data did not improved the performance of these models. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitalization.

CRT-200.11 Can A Simple Prediction Model Applied At Admission Predict Readmission Risk In CABG Patients?

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning. We developed different models to predict 30-

Enhancing care transfers from hospital to home for older people with complex needs

Introduction Care transfers for older people with complex needs should be person-centred with effective multi -disciplinary teamwork (MDT) across hospital and community settings (Bolsch et al. 2005). This paper presents a project to enhance care transfers within Southwark and Lambeth Integrated Care (SLIC), which is a federation that promotes integrated care in south London, through bringing together general practices, acute hospitals, mental health care, social care providers and commissioning groups. This project comprised: Phase 1: Literature review of best practice in care transfers of older people; Phase 2: Scoping staff educational needs through: meetings with key individuals and teams, observation of MDT meetings; analysis of a local patient discharge survey; Phase 3: Development, delivery and evaluation of a one day interprofessional simulation course (‘Good to go: enhancing care transfers for older people’) for health and social care professionals. Phases 1 and 2 findings indicated the need for understanding integrated care and service provision in the community; mutual trust, honesty and respect; communicating about patients across settings; and understanding local services and referral processes. There was strong support for simulation using complex patient scenarios, delivered interprofessionally, which can improve understanding of each profession’s role (Tofil et al. 2014). This paper will focus on the Phase 3 courses, their delivery and evaluation. Short description of practice change implemented The course aims were to:  Draw upon shared experience and knowledge to promote best practice for the safe transfer of care across a range of settings.  To enhance discharge planning skills including more effective communication, assessment and evaluation of needs, and multi-agency, inter-professional working. The course design reflected a patient’s journey from hospital to discharge and community care, based on real life local scenarios delivered with actors. The course’s target population was health and social care professionals across SLIC and whose roles included the care transfers of older people with complex needs. The six pilot courses (June-September 2015) were funded by a local education board. There was no backfill for course participants so engagement with key stakeholders (senior health and social care service managers) was essential for enabling attendance. Key findings A total of 49 staff attended and each cohort included varied professions across hospital and community settings, enabling them to learn together, build professional relationships and improve understanding of each other’s roles and services. Course evaluation was based on Kirkpatrick’s (1994) model: initial reaction to training; participants’ learning; application of learning from the training; and achievement of targeted results and outcomes. Participants completed questionnaires pre-course (n=44) and post-course (n=47) on the day, thus exploring the first two levels of Kirkpatrick’s model. Pre-course, 30 (68%) participants reported having experienced difficulty in transferring or receiving the care of a patient with complex needs. Post–course, 44 (91%) intended to make changes to their clinical practice and all believed these would enhance their MDT working. All agreed they understood the importance of effective communication and early communication. The open text comments included many about increased understanding of roles and communicating effectively across settings. Further evaluation is currently in progress using semi-structured interviews with a purposive sample of 12-15 course participants, exploring application of their learning from the course in practice and perceived outcomes (Kirkpatrick’s model, levels 3 and 4). These results will also be presented. Highlights The course aims were achieved, with a positive evaluation and the value of interprofessional attendance from community and hospital settings was evident, supporting recommendations for staff to have opportunities to gain better understanding of each other’s roles and build relationships (Baillie et al. 2014). One course was poorly attended due to late dropouts and the original project timeline was extended to enable sufficient time for rostering staff attendance. It was originally hoped to co-deliver the course with an older person with complex needs or carer but this population was difficult to recruit from due to ongoing health issues. This remains an aspiration for future courses. The follow-up interviews are enabling exploration of perceived application and impact on practice from participants’ perspectives. Conclusion The interprofessional simulation course was developed from best practice review and local scoping and staff perspectives. The course could be transferred to other settings and has been shared through simulation networks. A challenge is that staff turnover across London is high but staff would be able to transfer learning to new organisations. Evaluation is essential as simulation course delivery is labour-intensive and has to be justifiable in economically constrained times.

Readiness for discharge from subacute mental health units for older people: Using available measures to support clinical decision-making.

Prior to introduction of the Health of the Nation Outcome Scale 65+ (HoNOS65) as a mandated measure, the three subacute mental health units for older people in the present study routinely used the Care Planning Assessment Tool (CPAT) for clinical review and discharge planning. The aims of the present study were to compare these two measures of behavioural change during subacute admissions, to examine associations with discharge readiness, and to assess their overall contributions to discharge planning decisions. This is a prospective, comparative measurement study. HoNOS65 (severity) and CPAT (frequency) behavioural subscale ratings were collected from admission to discharge for older patients with very severe and persistent behavioural and psychological symptoms of dementia. Readiness for discharge data (yes/no), collected from multidisciplinary review meetings, was used as the outcome in all analyses. In combination, these measures achieved only modest positive predictive value (52.8%) but good negative predictive value (90.4%). Consequently, patients above the cut-point on both measures are reasonably unlikely to be discharge ready. The combined use of a standard outcome measure of severity along with a specialized measure of frequency is recommended to support and enhance discharge planning decisions in this population.

Predicting readmission risk following coronary artery bypass surgery at the time of admission.

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning. We developed different models to predict 30-day inpatient readmission to our institution in patients who underwent CABG between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from STS Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent CABG between May 2013 and September 2015. Our cohort included 1277 CABG patients: 1159 in the derivation cohort and 1018 in the validation cohort. The discriminative ability of the admission model was reasonable (C-index of 0.673). The c-indices for the discharge and STS models were slightly better. (C-index of 0.700 and 0.714 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.641, 0.659 and 0.670 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.573, 0.605 and 0.595 respectively). Risk prediction models based on data available early on admission are predictive for readmission risk. Adding registry data did not improved the performance of these models. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitalization.

Drug therapy problems and medication discrepancies during care transitions in super-utilizers

ObjectivesFirst, to investigate the prevalence and types of drug therapy problems and medication discrepancies among super-utilizers, and associated patient characteristics. Second, to examine the outcomes of pharmacist recommendations and estimated cost avoidance through care transitions support focused on medication management. DesignRetrospective analysis of the pharmacist-led interventions as part of the SafeMed Program. SettingA large nonprofit health care system serving the major medically underserved areas in Memphis, Tennessee. ParticipantsThree hundred seventy-four super-utilizing SafeMed participants with multiple chronic conditions and polypharmacy. InterventionComprehensive medication review, medication therapy management, enhanced discharge planning, home visits, telephone follow-up, postdischarge medication reconciliation, and care coordination with physicians. Main outcome measuresTypes of drug therapy problems, outcomes of pharmacist recommendations, estimated cost avoided, medication discrepancies, and self-reported medication adherence. ResultsPrevalence of drug therapy problems and postdischarge medication discrepancies was 80.7% and 75.4%, respectively. The most frequently occurring drug therapy problems were enrollee not receiving needed medications (33.4%), underuse of medications (16.9%), and insufficient dose or duration (11.2%). Overall 50.8% of the pharmacist recommendations were accepted by physicians and patients, resulting in an estimated cost avoidance of $293.30 per drug therapy problem identified. Multivariate analysis indicated that participants with a higher number of comorbidities were more likely to have medication discrepancies (odds ratio 1.23 [95% CI 1.05–1.44]). Additional contributors to postdischarge medication discrepancies were difficulty picking up and paying for medications and not being given necessary prescriptions before discharge. ConclusionDrug therapy problems and medication discrepancies are common in super-utilizers with multiple chronic conditions and polypharmacy during transitions of care, and greater levels of comorbidity magnify risk. Pharmacist-led interventions in the SafeMed Program have demonstrated success in resolving enrollees' medication-related issues, resulting in substantial estimated cost savings. Preliminary evidence suggests that the SafeMed model's focus on medication management has great potential to improve outcomes while reducing costs for vulnerable super-utilizing populations nationwide.

So many options, where do we start? An overview of the care transitions literature

Health systems are faced with a large array of transitional care interventions and patient populations to whom such activities might apply. To summarize the health and utilization effects of transitional care interventions, and to identify common themes about intervention types, patient populations, or settings that modify these effects. PubMed and Cochrane Database of Systematic Reviews (January 1950-May 2014), reference lists, and technical advisors. Systematic reviews of transitional care interventions that reported hospital readmission as an outcome. We extracted transitional care procedures, patient populations, settings, readmissions, and health outcomes. We identified commonalities and compiled a narrative synthesis of emerging themes. Among 10 reviews of mixed patient populations, there was consistent evidence that enhanced discharge planning and hospital-at-home interventions reduced readmissions. Among 7 reviews in specific patient populations, transitional care interventions reduced readmission in patients with congestive heart failure and general medical populations. In general, interventions that reduced readmission addressed multiple aspects of the care transition, extended beyond hospital stay, and had the flexibility to accommodate individual patient needs. There was insufficient evidence on how caregiver involvement, transition to sites other than home, staffing, patient selection practices, or care settings modified intervention effects. Successful interventions are comprehensive, extend beyond hospital stay, and have the flexibility to respond to individual patient needs. The strength of evidence should be considered low because of heterogeneity in the interventions studied, patient populations, clinical settings, and implementation strategies.

Virtualizing living and working spaces: Proof of concept for a biomedical space-replication methodology

The physical spaces within which the work of health occurs – the home, the intensive care unit, the emergency room, even the bedroom – influence the manner in which behaviors unfold, and may contribute to efficacy and effectiveness of health interventions. Yet the study of such complex workspaces is difficult. Health care environments are complex, chaotic workspaces that do not lend themselves to the typical assessment approaches used in other industrial settings. This paper provides two methodological advances for studying internal health care environments: a strategy to capture salient aspects of the physical environment and a suite of approaches to visualize and analyze that physical environment. We used a Faro™ laser scanner to obtain point cloud data sets of the internal aspects of home environments. The point cloud enables precise measurement, including the location of physical boundaries and object perimeters, color, and light, in an interior space that can be translated later for visualization on a variety of platforms. The work was motivated by vizHOME, a multi-year program to intensively examine the home context of personal health information management in a way that minimizes repeated, intrusive, and potentially disruptive in vivo assessments. Thus, we illustrate how to capture, process, display, and analyze point clouds using the home as a specific example of a health care environment. Our work presages a time when emerging technologies facilitate inexpensive capture and efficient management of point cloud data, thus enabling visual and analytical tools for enhanced discharge planning, new insights for designers of consumer-facing clinical informatics solutions, and a robust approach to context-based studies of health-related work environments.

An ethnographic study of knowledge sharing across the boundaries between care processes, services and organisations: the contributions to ‘safe’ hospital discharge

BackgroundHospital discharge is a vulnerable stage in the patient pathway. Research highlights communication failures and the problems of co-ordination as resulting in delayed, poorly timed and unsafe discharges. The complexity of hospital discharge exemplifies the threats to patient safety found ‘between’ care processes and organisations. In developing this perspective, safe discharge is seen as relying upon enhanced knowledge sharing and collaboration between stakeholders, which can mitigate system complexity and promote safety.AimTo identify interventions and practices that support knowledge sharing and collaboration in the processes of discharge planning and care transition.SettingThe study was undertaken between 2011 and 2013 in two English health-care systems, each comprising an acute health-care provider, community and primary care providers, local authority social services and social care agencies. The study sites were selected to reflect known variations in local population demographics as well as in the size and composition of the care systems. The study compared the experiences of stroke and hip fracture patients as exemplars of acute care with complex discharge pathways.DesignThe study involved in-depth ethnographic research in the two sites. This combined (a) over 180 hours of observations of discharge processes and knowledge-sharing activities in various care settings; (b) focused ‘patient tracking’ to trace and understand discharge activities across the entire patient journey; and (c) qualitative interviews with 169 individuals working in health, social and voluntary care sectors.FindingsThe study reinforces the view of hospital discharge as a complex system involving dynamic and multidirectional patterns of knowledge sharing between multiple groups. The study shows that discharge planning and care transitions develop through a series of linked ‘situations’ or opportunities for knowledge sharing. It also shows variations in these situations, in terms of the range of actors, forms of knowledge shared, and media and resources used, and the wider culture and organisation of discharge. The study also describes the threats to patient safety associated with hospital discharge, as perceived by participants and stakeholders. These related to falls, medicines, infection, clinical procedures, equipment, timing and scheduling of discharge, and communication. Each of these identified risks are analysed and explained with reference to the observed patterns of knowledge sharing to elaborate how variations in knowledge sharing can hinder or promote safe discharge.ConclusionsThe study supports the view of hospital discharge as a complex system involving tightly coupled and interdependent patterns of interaction between multiple health and social care agencies. Knowledge sharing can help to mitigate system complexity through supporting collaboration and co-ordination. The study suggests four areas of change that might enhance knowledge sharing, reduce system complexity and promote safety. First, knowledge brokers in the form of discharge co-ordinators can facilitate knowledge sharing and co-ordination; second, colocation and functional proximity of stakeholders can support knowledge sharing and mutual appreciation and alignment of divergent practices; third, local cultures should prioritise and value collaboration; and finally, organisational resources, procedures and leadership should be aligned to fostering knowledge sharing and collaborative working. These learning points provide insight for future interventions to enhance discharge planning and care transition. Future research might consider the implementation of interviews to mediate system complexity through fostering enhanced knowledge sharing across occupational and organisational boundaries. Research might also consider in more detail the underlying complexity of both health and social care systems and how opportunities for knowledge sharing might be engendered to promote patient safety in other areas.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

Abstract 231: Predicting Readmission Risk Following Coronary Revascularization at the Time of Admission

Objective: Reducing readmissions following hospitalization for coronary revascularization is a national priority. Identifying patients at high risk for readmission early in a hospitalization would enable hospitals to target these individuals for enhanced discharge planning. Traditional risk models identify patients based on characteristics that may not be available until after discharge. We sought to compare the model performance for models based on data that is available earlier in the hospitalization. Methods: We developed models to predict 30-day inpatient readmission to our instititution for a cohort of patients who were revascularized at our institution between January 2010 and December 2013. We developed separate models for patients with percutaneous coronary revascularization (PCI) and coronary artery bypass graft surgery (CABG). We developed three models using data available at three different time points in the hospitalization: 1) at admission, 2) early in the hospitalization and 3) at discharge. Candidate variables for the admission model included demographics, comorbidities, and previous utilization within our system. The second model added initial vital signs and laboratory values. The discharge model added discharge vital signs and laboratory values, discharge medications, new comorbidities, and length of stay. We assessed each model using the c-index. Results: Our cohort included 4,941 PCI patients and 1,633 CABG patients. The readmission rate was 8.4% in the PCI group and 14.2% in the CABG group. For both populations, the discriminative ability of the admission model was high (0.805 for PCI, 0.824 for CABG). The addition of laboratory values and vital signs was associated with slight improvement in discrimination (0.812 for PCI, 0.829 for CABG). The addition of data available at the time of discharge further increased model discrimination (0.833 for PCI, 0.848 for CABG). Except for one model, the strongest predictor of readmission was carrying a diagnosis of hypertension at baseline (OR &gt; 2 for all)). Previous hospitalization within six months was a strong predictor among PCI patients (OR 1.18, p&lt;0.001), and having a previous acute myocardial infarction was the strongest predictor among CABG patients (OR 2.46, p = 0.002). Discussion: Risk prediction models based on data available only at discharge minimally improved the performance of models based solely on demographic and utilization data available at the time of admission. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitlization. This would allow providers and health systems to target high-risk patients with enhanced discharge planning during the course of the hospitalization, and this may improve the ability to avoid readmissions.

Suicide Mortality Following Nursing Home Discharge in the Department of Veterans Affairs Health System

We assessed suicide rates up to 6 months following discharge from US Department of Veterans Affairs (VA) nursing homes. In VA Minimum Data Set (MDS) records, we identified 281 066 live discharges from the 137 VA nursing homes during fiscal years 2002 to 2008. We used MDS and administrative data to assess resident age, gender, behaviors, pain, and indications of psychoses, bipolar disorder, dementia, and depression. We identified vital status and suicide mortality within 6 months of discharge through National Death Index searches. Suicide rates within 6 months of discharge were 88.0 per 100 000 person-years for men and 89.4 overall. Standardized mortality ratios relative to age- and gender-matched individuals in the VA patient population were 2.3 for men (95% confidence interval [CI] = 1.9, 2.8) and 2.4 overall (95% CI = 2.0, 2.9). In multivariable proportional hazards regression analyses, resident characteristics, diagnoses, behaviors, and pain were not significantly associated with suicide risk. Suicide risk was elevated following nursing home discharge. This underscores the importance of ongoing VA efforts to enhance discharge planning and timely postdischarge follow-up.

The Health of Geriatrics in Canada —More Than Meets the Eye

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