Search

BackgroundTypical solutions for improving discharge planning often rely on one-way communication mechanisms, static data entry into the electronic health record (EHR), or in-person meetings. Lack of timely and effective communication can adversely affect patients and their care teams. ObjectiveApplying robust user-centered design strategies, we aimed to design an innovative EHR-based discharge readiness communication tool (the Discharge Today tool) to enable care teams to communicate any barriers to discharge, the status of patient discharge readiness, and patient discharge needs in real time across hospital settings. MethodsWe employed multiple user-centered design strategies, including exploration of the current state for documenting discharge readiness and directing discharge planning, iterative low-fidelity prototypes, multidisciplinary stakeholder meetings, a brainwriting premortem exercise, and preproduction user testing. We iteratively collected feedback from users via meetings and surveys. ResultsWe conducted 28 meetings with 20 different stakeholder groups. From these stakeholder meetings, we developed 14 low-fidelity prototypes prior to deploying the Discharge Today tool for our pilot study. During the pilot study, stakeholders requested 46 modifications, of which 25 (54%) were successfully executed. We found that most providers who responded to the survey reported that the tool either saved time or did not change the amount of time required to complete their discharge workflow (21/24, 88%). Responses to open-ended questions offered both positive feedback and opportunities for improvement in the domains of efficiency, integration into workflow, avoidance of redundancies, expedited communication, and patient-centeredness. ConclusionsSurvey data suggest that this electronic discharge readiness tool has been successfully adopted by providers and clinical staff. Frequent stakeholder engagement and iterative user-centered design were critical to the successful implementation of this tool.

BackgroundClinicians’ time with patients has become increasingly limited due to regulatory burden, documentation and billing, administrative responsibilities, and market forces. These factors limit clinicians’ time to deliver thorough explanations to patients. OpenNotes began as a research initiative exploring the ability of sharing medical notes with patients to help patients understand their health care. Providing patients access to their medical notes has been shown to have many benefits, including improved patient satisfaction and clinical outcomes. OpenNotes has since evolved into a national movement that helps clinicians share notes with patients. However, a significant barrier to the widespread adoption of OpenNotes has been clinicians’ concerns that OpenNotes may cost additional time to correct patient confusion over medical language. Recent advances in artificial intelligence (AI) technology may help resolve this concern by converting medical notes to plain language with minimal time required of clinicians. ObjectiveThis pilot study assesses patient comprehension and perceived benefits, concerns, and insights regarding an AI-simplified note through comprehension questions and guided interview. MethodsSynthea, a synthetic patient generator, was used to generate a standardized medical-language patient note which was then simplified using AI software. A multiple-choice comprehension assessment questionnaire was drafted with physician input. Study participants were recruited from inpatients at the University of Colorado Hospital. Participants were randomly assigned to be tested for their comprehension of the standardized medical-language version or AI-generated plain-language version of the patient note. Following this, participants reviewed the opposite version of the note and participated in a guided interview. A Student t test was performed to assess for differences in comprehension assessment scores between plain-language and medical-language note groups. Multivariate modeling was performed to assess the impact of demographic variables on comprehension. Interview responses were thematically analyzed. ResultsTwenty patients agreed to participate. The mean number of comprehension assessment questions answered correctly was found to be higher in the plain-language group compared with the medical-language group; however, the Student t test was found to be underpowered to determine if this was significant. Age, ethnicity, and health literacy were found to have a significant impact on comprehension scores by multivariate modeling. Thematic analysis of guided interviews highlighted patients’ perceived benefits, concerns, and suggestions regarding such notes. Major themes of benefits were that simplified plain-language notes may (1) be more useable than unsimplified medical-language notes, (2) improve the patient-clinician relationship, and (3) empower patients through an enhanced understanding of their health care. ConclusionsAI software may translate medical notes into plain-language notes that are perceived as beneficial by patients. Limitations included sample size, inpatient-only setting, and possible confounding factors. Larger studies are needed to assess comprehension. Insight from patient responses to guided interviews can guide the future study and development of this technology.

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.

BackgroundThe COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs).ObjectiveTo determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19.DesignRetrospective cohort.SettingEight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN).Target populationAdults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020.MeasurementsWe randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs.ResultsTwo hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p

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.

Multiple demands on a clinician's time and attention may contribute to burnout and medical errors. This scoping review explores the breadth of studies on physician attention, characterizing the studies by how they relate to clinician attention and clinical outcomes. The authors categorize and characterize studies along different characteristics such as the study methods, metrics used to measure attention, clinician studied, setting, and study intent to inform future research and review. Background: The concept of attention can provide insight into the needs of clinicians and how health systems design can impact patient care quality and medical errors. Purpose: To conduct a scoping review to 1) identify and characterize literature relevant to clinician attention; 2) compile metrics used to measure attention; and 3) create a framework of key concepts. Data Sources: Cumulated Index to Nursing and Allied Health Literature (CINAHL), Medline (PubMed), and Embase (Ovid) from 2001 to 26 February 2024. Study Selection: English-language studies addressing health care worker attention in patient care. At least dual review and data abstraction. Data Extraction: Article information, health care professional studied, practice environment, study design and intent, factor type related to attention, and metrics of attention used. Data Synthesis: Of 6448 screened articles, 585 met inclusion criteria. Most studies were descriptive (n = 469) versus investigational (n = 116). More studies focused on barriers to attention (n = 387; 342 descriptive and 45 investigational) versus facilitators to improving attention (n = 198; 112 descriptive and 86 investigational). We developed a framework, grouping studies into 6 categories: 1) definitions of attention, 2) the clinical environment and its effect on attention, 3) personal factors affecting attention, 4) relationships between interventions or factors that affect attention and patient outcomes, 5) the effect of clinical alarms and alarm fatigue on attention, and 6) health information technology's effect on attention. Eighty-two metrics were used to measure attention. Limitations: Does not synthesize answers to specific questions. Quality of studies was not assessed. Conclusion: This overview may be a resource for researchers, quality improvement experts, and health system leaders to improve clinical environments. Future systematic reviews may synthesize evidence on metrics to measure attention and on the effectiveness of barriers or facilitators related to attention. Primary Funding Source: None. [ABSTRACT FROM AUTHOR]

Background: The progression of patients through a hospital from admission to discharge can be slowed by delays in patient discharge, increasing pressure on health care staff. We designed and piloted the Discharge Today tool, with the goal of improving the efficiency of patient discharge; however, adoption remained low. Purpose: To close this implementation gap, we deployed and evaluated a 4-part implementation strategy bundle. Methods: We measured the success of implementation by evaluating validated implementation outcomes using both quantitative and qualitative methods, grounded in Normalization Process Theory. Results: The implementation strategies used were effective for increasing use of the Discharge Today tool by hospital medicine physicians and advanced practice providers during both the active and passive implementation periods. Conclusions: While the implementation strategies used were effective, qualitative findings indicate that limitations in the functionality of the tool, alongside inconsistent use of the tool across clinical staff, continued to inhibit adoption. [ABSTRACT FROM AUTHOR]

We assessed how hospitalists frame workplace safety, health, and well-being (SHW); their perception of hospital supports for SHW; and whether and how they are sharing leadership responsibility for each other's SHW. Our findings highlight the important role of local support for hospitalist SHW and reveal the systemic, hospital-wide problems that may impede their SHW. We believe that positioning hospitalists as leaders for SHW will result in systems-wide changes in practices to support the SHW of all care team members. (Am J Public Health. 2024;114(S2):S162–S166. https://doi.org/10.2105/AJPH.2024.307573) [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs). Objective: To determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19. Design: Retrospective cohort. Setting: Eight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN). Target population: Adults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020. Measurements: We randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs. Results: Two hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p < 0.01). Diagnostic process faults associated with policies and procedures related to COVID-19 were not associated with DE risk. Fourteen patients (35.9% of patients with errors and 5.4% overall) suffered harm or death due to diagnostic error. Limitations: Results are limited by available documentation and do not capture communication between providers and patients. Conclusion: Among PUI patients, DEs were common and not associated with pandemic-related care changes, suggesting the importance of more general diagnostic process gaps in error propagation. [ABSTRACT FROM AUTHOR]