Breaking down barriers: Decoding archetypes in hospital medicine research

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Perspectives on patient experience: A national survey of hospitalists

Despite efforts to improve patient experience (PX), little is known about the perspective of hospitalists regarding PX initiatives and priorities. A survey was distributed to hospitalist groups across the country assessing involvement in PX initiatives and their perceived effectiveness, what PX means to providers, and facilitators/barriers in improving PX. Ninety-nine percent of respondents had encountered some improvement activity around PX. The most prevalent were communication training, group Hospital Consumer Assessment of Healthcare Providers and Systems data, and interdisciplinary bedside rounding. Respondents rated most initiatives a 5 to 6 out of 10 for their effectiveness, with the perception of effectiveness increasing with respondents\u27 assessment of patient experience priority. Learning about others\u27 experiences in improving PX and learning about potential collaborations for quality improvement or research in these areas were areas of interest for future work. Qualitative work highlighted potential barriers in improving PX such as workload and staffing constraints, uncontrollable environmental factors, and unrealistic patient expectations. Improving PX is a priority, and there are many initiatives in place with perceived variable success and perceived barriers in improving PX

The Effectiveness of a Multidisciplinary Electronic Discharge Readiness Tool: Prospective, Single-Center, Pre-Post Study

BackgroundIn the face of hospital capacity strain, hospitals have developed multifaceted plans to try to improve patient flow. Many of these initiatives have focused on the timing of discharges and on lowering lengths of stay, and they have met with variable success. We deployed a novel tool in the electronic health record to enhance discharge communication. ObjectiveThe aim of this study is to evaluate the effectiveness of a discharge communication tool. MethodsThis was a prospective, single-center, pre-post study. Hospitalist physicians and advanced practice providers (APPs) used the Discharge Today Tool to update patient discharge readiness every morning and at any time the patient status changed throughout the day. Primary outcomes were tool use, time of day the clinician entered the discharge order, time of day the patient left the hospital, and hospital length of stay. We used linear mixed modeling and generalized linear mixed modeling, with team and discharging provider included in all the models to account for patients cared for by the same team and the same provider. ResultsDuring the pilot implementation period from March 5, 2019, to July 31, 2019, a total of 4707 patients were discharged (compared with 4558 patients discharged during the preimplementation period). A total of 352 clinical staff had used the tool, and 84.85% (3994/4707) of the patients during the pilot period had a discharge status assigned at least once. In a survey, most respondents reported that the tool was helpful (32/34, 94% of clinical staff) and either saved time or did not add additional time to their workflow (21/24, 88% of providers, and 34/34, 100% of clinical staff). Although improvements were not observed in either unadjusted or adjusted analyses, after including starting morning census per team as an effect modifier, there was a reduction in the time of day the discharge order was entered into the electronic health record by the discharging physician and in the time of day the patient left the hospital (decrease of 2.9 minutes per additional patient, P=.07, and 3 minutes per additional patient, P=.07, respectively). As an effect modifier, for teams that included an APP, there was a significant reduction in the time of day the patient left the hospital beyond the reduction seen for teams without an APP (decrease of 19.1 minutes per patient, P=.04). Finally, in the adjusted analysis, hospital length of stay decreased by an average of 3.7% (P=.06). ConclusionsThe Discharge Today tool allows for real time documentation and sharing of discharge status. Our results suggest an overall positive response by care team members and that the tool may be useful for improving discharge time and length of stay if a team is staffed with an APP or in higher-census situations

The Impact of Hospital Capacity Strain: a Qualitative Analysis of Experience and Solutions at 13 Academic Medical Centers

BackgroundHospital capacity strain impacts quality of care and hospital throughput and may also impact the well being of clinical staff and teams as well as their ability to do their job. Institutions have implemented a wide array of tactics to help manage hospital capacity strain with variable success.ObjectiveThrough qualitative interviews, our study explored interventions used to address hospital capacity strain and the perceived impact of these interventions, as well as how hospital capacity strain impacts patients, the workforce, and other institutional priorities.Design, setting, and participantsQualitative study utilizing semi-structured interviews at 13 large urban academic medical centers across the USA from June 21, 2019, to August 22, 2019 (pre-COVID-19). Interviews were recorded, professionally transcribed verbatim, coded, and then analyzed using a mixed inductive and deductive method at the semantic level.Main outcome measuresThemes and subthemes of semi-structured interviews were identified.ResultsTwenty-nine hospitalist leaders and hospital leaders were interviewed. Across the 13 sites, a multitude of provider, care team, and institutional tactics were implemented with perceived variable success. While there was some agreement between hospitalist leaders and hospital leaders, there was also some disagreement about the perceived successes of the various tactics deployed. We found three main themes: (1) hospital capacity strain is complex and difficult to predict, (2) the interventions that were perceived to have worked the best when facing strain were to ensure appropriate resources; however, less costly solutions were often deployed and this may lead to unanticipated negative consequences, and (3) hospital capacity strain and the tactics deployed may negatively impact the workforce and can lead to conflict.ConclusionsWhile institutions have employed many different tactics to manage hospital capacity strain and see this as a priority, tactics seen as having the highest yield are often not the first employed

Achieving diagnostic excellence through prevention and teamwork (ADEPT) study protocol: A multicenter, prospective quality and safety program to improve diagnostic processes in medical inpatients.

BACKGROUND: Few hospitals have built surveillance for diagnostic errors into usual care or used comparative quantitative and qualitative data to understand their diagnostic processes and implement interventions designed to reduce these errors. OBJECTIVES: To build surveillance for diagnostic errors into usual care, benchmark diagnostic performance across sites, pilot test interventions, and evaluate the programs impact on diagnostic error rates. METHODS AND ANALYSIS: Achieving diagnostic excellence through prevention and teamwork (ADEPT) is a multicenter, real-world quality and safety program utilizing interrupted time-series techniques to evaluate outcomes. Study subjects will be a randomly sampled population of medical patients hospitalized at 16 US hospitals who died, were transferred to intensive care, or had a rapid response during the hospitalization. Surveillance for diagnostic errors will occur on 10 events per month per site using a previously established two-person adjudication process. Concurrent reviews of patients who had a qualifying event in the previous week will allow for surveys of clinicians to better understand contributors to diagnostic error, or conversely, examples of diagnostic excellence, which cannot be gleaned from medical record review alone. With guidance from national experts in quality and safety, sites will report and benchmark diagnostic error rates, share lessons regarding underlying causes, and design, implement, and pilot test interventions using both Safety I and Safety II approaches aimed at patients, providers, and health systems. Safety II approaches will focus on cases where diagnostic error did not occur, applying theories of how people and systems are able to succeed under varying conditions. The primary outcome will be the number of diagnostic errors per patient, using segmented multivariable regression to evaluate change in y-intercept and change in slope after initiation of the program. ETHICS AND DISSEMINATION: The study has been approved by the University of California, San Francisco Institutional Review Board (IRB), which is serving as the single IRB. Intervention toolkits and study findings will be disseminated through partners including Vizient, The Joint Commission, and Press-Ganey, and through national meetings, scientific journals, and publications aimed at the general public