Redesigning inpatient care: testing the effectiveness of an Accountable Care Team model

BACKGROUND US healthcare underperforms on quality and safety metrics. Inpatient care constitutes an immense opportunity to intervene to improve care. OBJECTIVE Describe a model of inpatient care and measure its impact. DESIGN A quantitative assessment of the implementation of a new model of care. The graded implementation of the model allowed us to follow outcomes and measure their association with the dose of the implementation. SETTING AND PATIENTS Inpatient medical and surgical units in a large academic health center. INTERVENTION Eight interventions rooted in improving interprofessional collaboration (IPC), enabling data-driven decisions, and providing leadership were implemented. MEASUREMENTS Outcome data from August 2012 to December 2013 were analyzed using generalized linear mixed models for associations with the implementation of the model. Length of stay (LOS) index, case-mix index–adjusted variable direct costs (CMI-adjusted VDC), 30-day readmission rates, overall patient satisfaction scores, and provider satisfaction with the model were measured. RESULTS The implementation of the model was associated with decreases in LOS index (P < 0.0001) and CMI-adjusted VDC (P = 0.0006). We did not detect improvements in readmission rates or patient satisfaction scores. Most providers (95.8%, n = 92) agreed that the model had improved the quality and safety of the care delivered. CONCLUSIONS Creating an environment and framework in which IPC is fostered, performance data are transparently available, and leadership is provided may improve value on both medical and surgical units. These interventions appear to be well accepted by front-line staff. Readmission rates and patient satisfaction remain challenging

Implementation of an Innovative Early Warning System: Evidenced-based Strategies for Ensuring System-wide Nursing Adoption

Early deterioration in adult medical-surgical patients is associated with increased intensive care unit and hospital mortality (Goldhill, 2001). Failure to recognize deterioration is a preventable patient safety and quality issue. To address this problem, since 2013, Kaiser Permanente Northern California (KP NCAL) has piloted Advance Alert Monitor (AAM) at two hospitals. This early warning system employs a set of predictive models developed by the KP NCAL Division of Research, which automatically predicts patient deterioration within the next 12 hours based on a complex algorithm of laboratory and clinical data points. Improvements in mortality and length of stay have been realized at the two pilot hospitals. In anticipation of expansion to additional NCAL facilities, major changes to the AAM workflows and processes were developed that increased the sensitivity of the patients identified at risk for clinical deterioration, as well as the timeliness and clarity of clinical response. Expansion to two additional pilot hospitals using these revised processes rely on the evidence-based implementation strategies found in this Doctor of Nursing Practice project. This paper examines the planning, assessment, and implementation of early warning systems at two NCAL facilities using Rogers’ diffusion of innovation theory and Greenhalgh’s extension of Rogers’ theory. Key attributes need to be considered from a cultural and organizational perspective to both start and sustain an implementation. The success of AAM implementation is validated using specific outcome and process measures, including compliance with documentation and timeliness of workflows

Educating to the Collaborative Care Model

The problem addressed in this project was the lack of experienced RNs needed in the acute care setting to deliver safe, quality patient care, while effectively managing resources and providing job satisfaction. The purpose of this project was to determine if an education module designed to educate charge and rover nurses on the Collaborative Care Model (CCM) would enhance staff nurses\u27 abilities to provide safe, high quality care to patients, and improve staff nurse retention on one unit in an acute care setting. The theoretical frameworks utilized to guide the education module included: Lewin\u27s theory of planned change, Benner\u27s novice to expert model, and AACN\u27s synergy model for patient care. The project question asked if an educative process designed around the CCM for charge nurses and rovers would result in improvement and sustainment of nursing quality indicators on the unit and improve staff nurse retention. The educational modules included two, four-hour education sessions with power point presentations and interactive assignments presented on two separate dates. Analysis of effectiveness was determined by comparing initial and post education nursing quality indicators (Hospital Consumer Assessment of Healthcare Providers & Systems Dashboard and the Human Resources Score Card) for the unit. Results showed that staff turnover was reduced from 41% to 35.9% and patients\u27 perceptions of teamwork increased from 47.4% to 60.9% following the education modules. This project contributes to positive social change by providing education to promote quality care and staff nurse retention

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

BACKGROUND: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. METHODS: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. RESULTS: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. CONCLUSIONS: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. TRIAL REGISTRATION: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

BACKGROUND: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. METHODS: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. RESULTS: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. CONCLUSIONS: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. TRIAL REGISTRATION: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR

The Design, implementation and Evaluation of a Technology Solution to Improve Discharge Planning Communication in a Complex Patient Population

Unnecessary delays in discharge planning can extend the length of stay (LOS) and add non-reimbursable days for socially and medically complex patients thereby increasing the financial burden to healthcare organizations. The literature supports enhanced discharge communication strategies and the use of checklists to facilitate safe and timely discharges. Following root cause analyses of significant discharge delays, one hospital identified gaps in communication as key precursors associated with discharge planning breakdown when discharging patients to skilled nursing facilities. Review of these events demonstrated the need for concurrent communication strategies between multidisciplinary care team members in planning for complex discharges. Following a complete assessment of the current discharge planning process, a web-based interactive discharge checklist was designed, implemented and evaluated in the attempt to provide guided communications to the essential partners of the patient’s team in an effort to reduce LOS and readmissions. After a six-month rollout of the new technology and concomitant procedures, the analyses revealed improvement in both the patient’s perception of discharge planning and the ability to discharge patients by noon. Results for LOS and readmission demonstrated inconsistent improvement. The use of an electronic checklist as a communication tool did reduce variability in discharge procedures and provided for continuity in handoff communication between team members. Staff agreed it promoted continuity and improved efficiency

Managing the Prevention of In-Hospital Resuscitation by Early Detection and Treatment of High-Risk Patients

In hospitalized patients, cardiorespiratory collapse mostly occurs after a distinct period of deterioration. This deterioration can be discovered by a systematic quantification of a set of clinical parameters. The combination of such a detection system—to identify patients at risk in an early stage —and a rapid response team—which can intervene immediately—can be implemented to prevent life-threatening situations and reduce the incidence of in-hospital cardiac arrests outside the intensive care setting. The effectiveness of both of these systems is influenced by the used trigger criteria, the number of rapid response team (RRT) activations, the in- or exclusion of patients with a DNR code >3, proactive rounding, the team composition, and its response time. Each of those elements should be optimized for maximal efficacy, and both systems need to work in tandem with little delay between patient deterioration, accurate detection, and swift intervention. Dependable diagnostics and scoring protocols must be implemented, as well as the organization of a 24/7 vigilant and functional experienced RRT. This implies a significant financial investment to provide an only sporadically required fast intervention and sustained alertness of the people involved

Dashboard Confessional: Co-Designing a decision-making support tool to support Resident's test ordering

Master of DesignArt and DesignUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/156122/1/DeepBlue_Jesko_2020_MDes_Thesis.pd