Adverse diagnostic events in hospitalised patients: a single-centre, retrospective cohort study.

BACKGROUND: Adverse event surveillance approaches underestimate the prevalence of harmful diagnostic errors (DEs) related to hospital care. METHODS: We conducted a single-centre, retrospective cohort study of a stratified sample of patients hospitalised on general medicine using four criteria: transfer to intensive care unit (ICU), death within 90 days, complex clinical events, and none of the aforementioned high-risk criteria. Cases in higher-risk subgroups were over-sampled in predefined percentages. Each case was reviewed by two adjudicators trained to judge the likelihood of DE using the Safer Dx instrument; characterise harm, preventability and severity; and identify associated process failures using the Diagnostic Error Evaluation and Research Taxonomy modified for acute care. Cases with discrepancies or uncertainty about DE or impact were reviewed by an expert panel. We used descriptive statistics to report population estimates of harmful, preventable and severely harmful DEs by demographic variables based on the weighted sample, and characteristics of harmful DEs. Multivariable models were used to adjust association of process failures with harmful DEs. RESULTS: Of 9147 eligible cases, 675 were randomly sampled within each subgroup: 100% of ICU transfers, 38.5% of deaths within 90 days, 7% of cases with complex clinical events and 2.4% of cases without high-risk criteria. Based on the weighted sample, the population estimates of harmful, preventable and severely harmful DEs were 7.2% (95% CI 4.66 to 9.80), 6.1% (95% CI 3.79 to 8.50) and 1.1% (95% CI 0.55 to 1.68), respectively. Harmful DEs were frequently characterised as delays (61.9%). Severely harmful DEs were frequent in high-risk cases (55.1%). In multivariable models, process failures in assessment, diagnostic testing, subspecialty consultation, patient experience, and history were significantly associated with harmful DEs. CONCLUSIONS: We estimate that a harmful DE occurred in 1 of every 14 patients hospitalised on general medicine, the majority of which were preventable. Our findings underscore the need for novel approaches for adverse DE surveillance.

Development of MyREADY Transition BBD Mobile App, a Health Intervention Technology Platform, to Improve Care Transition for Youth With Brain-Based Disabilities: User-Centered Design Approach.

BACKGROUND: Transition from pediatric to adult health care varies and is resource intensive. Patient-centered health information technology (HIT) interventions are increasingly being developed in partnership with patients. OBJECTIVE: This study aims to develop an internet-based mobile app intervention for patients with brain-based disabilities to improve transition in care readiness. METHODS: The app was designed for patients aged 15 to 17 years with brain-based disabilities having the ability to use a mobile app. A multidisciplinary team, an industry partner, and a patient and family advisory council was assembled. We hypothesized that existing tools could be migrated into the app to address education, empowerment, and navigation. We used cognitive learning theory to support chapters targeting transition in care skill sets. We used the agile iterative methodology to engage stakeholders. RESULTS: We developed a novel MyREADY Transition HIT platform. An electronic mentor supported cognitive learning with messaging, quizzes, rewards, and videos. We used gaming to guide navigation through a fictitious health care city. Adapting existing tools was achieved by the patient and family advisory council requesting personalization. Our iterative design required time-consuming back-end technology management. Developing the platform took 24 months instead of our grant-approved 12 months, impacting the onset of the planned trial within the allotted budget. CONCLUSIONS: A novel patient-centered HIT platform to improve health care transition was successfully developed in partnership with patients and industry. Careful resource management was needed to achieve timely delivery of the end product, flagging the cautious planning required to deliver HIT tools in time for the much-needed trials informing their clinical application. TRIAL REGISTRATION: ClinicalTrials.gov NCT03852550; https://clinicaltrials.gov/study/NCT03852550.

Patient-Clinician Diagnostic Concordance upon Hospital Admission.

OBJECTIVES: This study aimed to pilot an application-based patient diagnostic questionnaire (PDQ) and assess the concordance of the admission diagnosis reported by the patient and entered by the clinician. METHODS: Eligible patients completed the PDQ assessing patients' understanding of and confidence in the diagnosis 24 hours into hospitalization either independently or with assistance. Demographic data, the hospital principal problem upon admission, and International Classification of Diseases 10th Revision (ICD-10) codes were retrieved from the electronic health record (EHR). Two physicians independently rated concordance between patient-reported diagnosis and clinician-entered principal problem as full, partial, or no. Discrepancies were resolved by consensus. Descriptive statistics were used to report demographics for concordant (full) and nonconcordant (partial or no) outcome groups. Multivariable logistic regressions of PDQ questions and a priori selected EHR data as independent variables were conducted to predict nonconcordance. RESULTS: A total of 157 (77.7%) questionnaires were completed by 202 participants; 77 (49.0%), 46 (29.3%), and 34 (21.7%) were rated fully concordant, partially concordant, and not concordant, respectively. Cohen's kappa for agreement on preconsensus ratings by independent reviewers was 0.81 (0.74, 0.88). In multivariable analyses, patient-reported lack of confidence and undifferentiated symptoms (ICD-10 "R-code") for the principal problem were significantly associated with nonconcordance (partial or no concordance ratings) after adjusting for other PDQ questions (3.43 [1.30, 10.39], p = 0.02) and in a model using selected variables (4.02 [1.80, 9.55], p < 0.01), respectively. CONCLUSION: About one-half of patient-reported diagnoses were concordant with the clinician-entered diagnosis on admission. An ICD-10 "R-code" entered as the principal problem and patient-reported lack of confidence may predict patient-clinician nonconcordance early during hospitalization via this approach.

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.

Background: Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. Objective: This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. Methods: The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient's OUD risk level, which was then compared to the ML application's risk assignments. Results: A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. Conclusions: A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.

Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians.

STUDY OBJECTIVE: The workload of clinical documentation contributes to health care costs and professional burnout. The advent of generative artificial intelligence language models presents a promising solution. The perspective of clinicians may contribute to effective and responsible implementation of such tools. This study sought to evaluate 3 uses for generative artificial intelligence for clinical documentation in pediatric emergency medicine, measuring time savings, effort reduction, and physician attitudes and identifying potential risks and barriers. METHODS: This mixed-methods study was performed with 10 pediatric emergency medicine attending physicians from a single pediatric emergency department. Participants were asked to write a supervisory note for 4 clinical scenarios, with varying levels of complexity, twice without any assistance and twice with the assistance of ChatGPT Version 4.0. Participants evaluated 2 additional ChatGPT-generated clinical summaries: a structured handoff and a visit summary for a family written at an 8th grade reading level. Finally, a semistructured interview was performed to assess physicians' perspective on the use of ChatGPT in pediatric emergency medicine. Main outcomes and measures included between subjects' comparisons of the effort and time taken to complete the supervisory note with and without ChatGPT assistance. Effort was measured using a self-reported Likert scale of 0 to 10. Physicians' scoring of and attitude toward the ChatGPT-generated summaries were measured using a 0 to 10 Likert scale and open-ended questions. Summaries were scored for completeness, accuracy, efficiency, readability, and overall satisfaction. A thematic analysis was performed to analyze the content of the open-ended questions and to identify key themes. RESULTS: ChatGPT yielded a 40% reduction in time and a 33% decrease in effort for supervisory notes in intricate cases, with no discernible effect on simpler notes. ChatGPT-generated summaries for structured handoffs and family letters were highly rated, ranging from 7.0 to 9.0 out of 10, and most participants favored their inclusion in clinical practice. However, there were several critical reservations, out of which a set of general recommendations for applying ChatGPT to clinical summaries was formulated. CONCLUSION: Pediatric emergency medicine attendings in our study perceived that ChatGPT can deliver high-quality summaries while saving time and effort in many scenarios, but not all.

Navigating meaningful engagement: lessons from partnering with youth and families in brain-based disability research.

BACKGROUND: While patient and family engagement in research has become a widespread practice, meaningful and authentic engagement remains a challenge. In the READYorNot™ Brain-Based Disabilities Study, we developed the MyREADY Transition™ Brain-Based Disabilities App to promote education, empowerment, and navigation for the transition from pediatric to adult care among youth with brain-based disabilities, aged 15-17 years old. Our research team created a Patient and Family Advisory Council (PFAC) to engage adolescents, young adults, and parent caregivers as partners throughout our multi-year and multi-stage project. MAIN BODY: This commentary, initiated and co-authored by members of our PFAC, researchers, staff, and a trainee, describes how we corrected the course of our partnership in response to critical feedback from partners. We begin by highlighting an email testimonial from a young adult PFAC member, which constituted a "critical turning point," that unveiled feelings of unclear expectations, lack of appreciation, and imbalanced relationships among PFAC members. As a team, we reflected on our partnership experiences and reviewed documentation of PFAC activities. This process allowed us to set three intentions to create a collective goal of authentic and meaningful engagement and to chart the course to get us there: (1) offering clarity and flexibility around participation; (2) valuing and acknowledging partners and their contributions; and (3) providing choice and leveraging individual interests and strengths. Our key recommendations include: (1) charting the course with a plan to guide our work; (2) learning the ropes by developing capacity for patient-oriented research; (3) all hands on deck by building a community of engagement; and (4) making course corrections and being prepared to weather the storms by remaining open to reflection, re-evaluation, and adjustment as necessary. CONCLUSIONS: We share key recommendations and lessons learned from our experiences alongside examples from the literature to offer guidance for multi-stage research projects partnering with adolescents, young adults, and family partners. We hope that by sharing challenges and lessons learned, we can help advance patient and family engagement in research.

What Went Right? A Mixed-Methods Study of Positive Feedback Data in a Hospital-Wide Mortality Review Survey.

BACKGROUND: Toxic work culture contributes to healthcare worker burnout and attrition, but little is known about how healthcare organizations can systematically create and promote a culture of civility and collegiality. OBJECTIVE: To analyze peer-to-peer positive feedback collected as part of a systematized mortality review survey to identify themes and recognition dynamics that can inform positive organizational culture change. DESIGN: Convergent mixed-methods study design. PARTICIPANTS: A total of 388 physicians, 212 registered nurses, 64 advanced practice providers, and 1 respiratory therapist at four non-profit hospitals (2 academic and 2 community). INTERVENTION: Providing optional positive feedback in the mortality review survey. MAIN MEASURES: Key themes and subthemes that emerged from positive feedback data, associations between key themes and positive feedback respondent characteristics, and recognition dynamics between positive feedback respondents and recipients. KEY RESULTS: Approximately 20% of healthcare workers provided positive feedback. Three key themes emerged among responses with free text comments: (1) providing extraordinary patient and family-centered care; (2) demonstrating self-possession and mastery; and (3) exhibiting empathic peer support and effective team collaboration. Compared to other specialties, most positive feedback from medicine (70.2%), neurology (65.2%), hospice and palliative medicine (64.3%), and surgery (58.8%) focused on providing extraordinary patient and family-centered care (p = 0.02), whereas emergency medicine (59.1%) comments predominantly focused on demonstrating self-possession and mastery (p = 0.06). Registered nurses (40.2%) provided multidirectional positive feedback more often than other clinician types in the hospital hierarchy (p < 0.001). CONCLUSIONS: Analysis of positive feedback from a mortality review survey provided meaningful insights into a health system's culture of teamwork and values related to civility and collegiality when providing end-of-life care. Systematic collection and sharing of positive feedback is feasible and has the potential to promote positive culture change and improve healthcare worker well-being.

Implementation of a Scalable Online Weight Management Programme in Clinical Settings: Protocol for the PROPS 2.0 Programme (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0).

INTRODUCTION: There is an urgent need for scalable strategies for treating overweight and obesity in clinical settings. PROPS 2.0 (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0) aims to adapt and implement the combined intervention from the PROPS Study at scale, in a diverse cross-section of patients and providers. METHODS AND ANALYSIS: We are implementing PROPS 2.0 across a variety of clinics at Brigham and Women's Hospital, targeting enrolment of 5000 patients. Providers can refer patients or patients can self-refer. Eligible patients must be ≥20 years old and have a body mass index (BMI) of ≥30 kg/m2 or a BMI of 25-29.9 kg/m2 plus another cardiovascular risk factor or obesity-related condition. After enrolment, patients register for the RestoreHealth online programme/app (HealthFleet Inc.) and participate for 12 months. Patients can engage with the programme and receive personalized feedback from a coach. Patient navigators help to enrol patients, enter updates in the electronic health record, and refer patients to additional resources. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is guiding the evaluation. ETHICS AND DISSEMINATION: The Mass General Brigham Human Research Committee approved this protocol. An implementation guide will be created and disseminated, to help other sites adopt the intervention in the future. TRIAL REGISTRATION NUMBER: NCT0555925.

Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach.

Objective: To describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients. Materials and Methods: Three interventions were prioritized for development: a Diagnostic Safety Column (DSC) within an EHR-integrated dashboard to identify at-risk patients; a Diagnostic Time-Out (DTO) for clinicians to reassess the working diagnosis; and a Patient Diagnosis Questionnaire (PDQ) to gather patient concerns about the diagnostic process. Initial requirements were refined from analysis of test cases with elevated risk predicted by DSC logic compared to risk perceived by a clinician working group; DTO testing sessions with clinicians; PDQ responses from patients; and focus groups with clinicians and patient advisors using storyboarding to model the integrated interventions. Mixed methods analysis of participant responses was used to identify final requirements and potential implementation barriers. Results: Final requirements from analysis of 10 test cases predicted by the DSC, 18 clinician DTO participants, and 39 PDQ responses included the following: DSC configurable parameters (variables, weights) to adjust baseline risk estimates in real-time based on new clinical data collected during hospitalization; more concise DTO wording and flexibility for clinicians to conduct the DTO with or without the patient present; and integration of PDQ responses into the DSC to ensure closed-looped communication with clinicians. Analysis of focus groups confirmed that tight integration of the interventions with the EHR would be necessary to prompt clinicians to reconsider the working diagnosis in cases with elevated diagnostic error (DE) risk or uncertainty. Potential implementation barriers included alert fatigue and distrust of the risk algorithm (DSC); time constraints, redundancies, and concerns about disclosing uncertainty to patients (DTO); and patient disagreement with the care team's diagnosis (PDQ). Discussion: A user-centered approach led to evolution of requirements for 3 interventions targeting key diagnostic process failures in hospitalized patients at risk for DE. Conclusions: We identify challenges and offer lessons from our user-centered design process.

Using Failure Mode and Effect Analysis to Identify Potential Failures in a Psychiatric Hospital Emergency Department.

OBJECTIVES: Failure mode and effect analysis (FMEA) is a powerful tool for accessing potential failures, but the participants are limited. It has not been used in psychiatric hospitals. Objectives were to implement FMEA in a psychiatric hospital and determine whether the FMEA process can be expanded by including participants who are familiar with the emergency department (ED) admission process and those who are not. METHODS: In this prospective, questionnaire-based study, a multidisciplinary team experienced in ED admissions was trained in FMEA and determined potential failures in the process. They developed a questionnaire regarding the failures, which were ranked by 17 ED and 28 non-ED healthcare providers. Risk priority numbers were calculated for each. RESULTS: By applying FMEA, we found 6 steps of the ED admission process, with 32 potential failures. Risk priority numbers ranged from 91 to 225. The most notable potential failure identified was during a patient's initial telephone call to the ED, before arrival. Emergency department and non-ED workers ranked 94% of the potential failures similarly. CONCLUSIONS: Failure mode and effect analysis can be implemented in psychiatric hospitals and can be a useful tool for anticipating potential failures. The number of participants in an FMEA can be increased to include those who are not directly involved in the process and should involve several specialists from diverse fields. Increasing the number of participants allows more detailed analyses. A checklist detailing the actions to take when processing a patient's initial phone call should be implemented to decrease hazards related to ED admissions.

Correction: Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

[This corrects the article DOI: 10.2196/43960.].

Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

BACKGROUND: Evidence-based point-of-care information (POCI) tools can facilitate patient safety and care by helping clinicians to answer disease state and drug information questions in less time and with less effort. However, these tools may also be visually challenging to navigate or lack the comprehensiveness needed to sufficiently address a medical issue. OBJECTIVE: This study aimed to collect clinicians' feedback and directly observe their use of the combined POCI tool DynaMed and Micromedex with Watson, now known as DynaMedex. EBSCO partnered with IBM Watson Health, now known as Merative, to develop the combined tool as a resource for clinicians. We aimed to identify areas for refinement based on participant feedback and examine participant perceptions to inform further development. METHODS: Participants (N=43) within varying clinical roles and specialties were recruited from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts, United States, between August 10, 2021, and December 16, 2021, to take part in usability sessions aimed at evaluating the efficiency and effectiveness of, as well as satisfaction with, the DynaMed and Micromedex with Watson tool. Usability testing methods, including think aloud and observations of user behavior, were used to identify challenges regarding the combined tool. Data collection included measurements of time on task; task ease; satisfaction with the answer; posttest feedback on likes, dislikes, and perceived reliability of the tool; and interest in recommending the tool to a colleague. RESULTS: On a 7-point Likert scale, pharmacists rated ease (mean 5.98, SD 1.38) and satisfaction (mean 6.31, SD 1.34) with the combined POCI tool higher than the physicians, nurse practitioner, and physician's assistants (ease: mean 5.57, SD 1.64, and satisfaction: mean 5.82, SD 1.60). Pharmacists spent longer (mean 2 minutes, 26 seconds, SD 1 minute, 41 seconds) on average finding an answer to their question than the physicians, nurse practitioner, and physician's assistants (mean 1 minute, 40 seconds, SD 1 minute, 23 seconds). CONCLUSIONS: Overall, the tool performed well, but this usability evaluation identified multiple opportunities for improvement that would help inexperienced users.

Providers' and Patients' Perspectives on Diagnostic Errors in the Acute Care Setting.

BACKGROUND: Diagnostic errors (DEs) have been studied extensively in ambulatory care, but less work has been done in the acute care setting. In this study, the authors examined health care providers' and patients' perspectives about the classification of DEs, the main causes and scope of DEs in acute care, the main gaps in current systems, and the need for innovative solutions. METHODS: A qualitative mixed methods study was conducted, including semistructured interviews with health care providers and focus groups with patient advisors. Using grounded theory approach, thematic categories were derived from the interviews and focus groups. RESULTS: The research team conducted interviews with 17 providers and two focus groups with seven patient advisors. Both providers and patient advisors struggled to define and describe DEs in acute care settings. Although participants agreed that DEs pose a significant risk to patient safety, their perception of the frequency of DEs was mixed. Most participants identified communication failures, lack of comfort with diagnostic uncertainty, incorrect clinical evaluation, and cognitive load as key causes of DEs. Most respondents believed that non-information technology (IT) tools and processes (for example, communication improvement strategies) could significantly reduce DEs. CONCLUSION: The study findings represent an important supplement to our understanding of DEs in acute care settings and the advancement of a culture of patient safety in the context of patient-centered care and patient engagement. Health care organizations should consider the key factors identified in this study when trying to create a culture that engages clinicians and patients in reducing DEs.

Comparison of a Voluntary Safety Reporting System to a Global Trigger Tool for Identifying Adverse Events in an Oncology Population.

OBJECTIVE: There is a lack of research on adverse event (AE) detection in oncology patients, despite the propensity for iatrogenic harm. Two common methods include voluntary safety reporting (VSR) and chart review tools, such as the Institute for Healthcare Improvement's Global Trigger Tool (GTT). Our objective was to compare frequency and type of AEs detected by a modified GTT compared with VSR for identifying AEs in oncology patients in a larger clinical trial. METHODS: Patients across 6 oncology units (from July 1, 2013, through May 29, 2015) were randomly selected. Retrospective chart reviews were conducted by a team of nurses and physicians to identify AEs using the GTT. The VSR system was queried by the department of quality and safety of the hospital. Adverse event frequencies, type, and harm code for both methods were compared. RESULTS: The modified GTT detected 0.90 AEs per patient (79 AEs in 88 patients; 95% [0.71-1.12] AEs per patient) that were predominantly medication AEs (53/79); more than half of the AEs caused harm to the patients (41/79, 52%), but only one quarter were preventable (21/79; 27%). The VSR detected 0.24 AEs per patient (21 AEs in 88 patients; 95% [0.15-0.37] AEs per patient), a large plurality of which were medication/intravenous related (8/21); more than half did not cause harm (70%). Only 2% of the AEs (2/100) were detected by both methods. CONCLUSIONS: Neither the modified GTT nor the VSR system alone is sufficient for detecting AEs in oncology patient populations. Further studies exploring methods such as automated AE detection from electronic health records and leveraging patient-reported AEs are needed.

Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study.

Objective: Many options are currently available for sepsis surveillance clinical decision support (CDS) from electronic medical record (EMR) vendors, third party, and homegrown models drawing on rule-based (RB) and machine learning (ML) algorithms. This study explores sepsis CDS implementation from the perspective of implementation leads by describing the motivations, tool choices, and implementation experiences of a diverse group of implementers. Materials and Methods: Semi-structured interviews were conducted with and a questionnaire was administered to 21 hospital leaders overseeing CDS implementation at 15 US medical centers. Participants were recruited via convenience sampling. Responses were coded by 2 coders with consensus approach and inductively analyzed for themes. Results: Use of sepsis CDS is motivated in part by quality metrics for sepsis patients. Choice of tool is driven by ease of integration, customization capability, and perceived predictive potential. Implementation processes for these CDS tools are complex, time-consuming, interdisciplinary undertakings resulting in heterogeneous choice of tools and workflow integration. To improve clinician acceptance, implementers addressed both optimization of the alerts as well as clinician understanding and buy in. More distrust and confusion was reported for ML models, as compared to RB models. Respondents described a variety of approaches to overcome implementation barriers; these approaches related to alert firing, content, integration, and buy-in. Discussion: While there are shared socio-technical challenges of implementing CDS for both RB and ML models, attention to user education, support, expectation management, and dissemination of effective practices may improve feasibility and effectiveness of ML models in quality improvement efforts. Conclusion: Further implementation science research is needed to determine real world efficacy of these tools. Clinician acceptance is a significant barrier to sepsis CDS implementation. Successful implementation of less clinically intuitive ML models may require additional attention to user confusion and distrust.

Primary care patients' and providers' perspectives about an online weight management program integrated with population health management: Post-intervention qualitative results from the PROPS study.

Objective: To assess patients' and providers' attitudes about the online weight management program and population health management approach in the PROPS Study, which examined the effectiveness of these strategies in primary care. Methods: We conducted semi-structured interviews with 22 patients and nine providers. Using thematic analysis, we analyzed transcripts of the interviews to identify key themes. Results: Most patients found the online program to be well-structured and easy to use, although a few noted that the information was overwhelming or could be more personalized. Patients mentioned that the support from the population health managers was critical for their success, and several reported that they would have liked more involvement from their primary care provider or a dietitian. Providers also were satisfied with the interventions, and several stated that the population health management support was helpful because it added accountability. Providers suggested that the interventions could be improved by tailoring the information and integrating the online program with the electronic health record. Conclusion: Most patients and providers were satisfied with the interventions, with several recommendations for improvements. Innovation: These findings give additional information about patients' and providers' experience with this innovative approach for managing overweight and obesity in primary care.

Patient safety and staff psychological safety: A mixed methods study on aspects of teamwork in the operating room.

Objectives: To predict the amount of teamwork that takes place throughout a surgery, based on performing a preoperative safety standards (surgical safety checklist and surgical count) and to explore factors affecting patient safety and staff psychological safety during a surgery, based on interprofessional teamwork. Methods: This mixed methods study included quantitative and qualitative analyses. Quantitative data included 2,184 direct observations of surgical cases with regard to the performance of safety standards during surgeries in 29 hospitals, analyzed using multivariate binary logistic regressions. Qualitative data were obtained from an analysis of 25 semi-structured interviews with operating room (OR) clinicians and risk managers, using an inductive thematic analysis approach. Results: Analysis of the OR observations revealed that a lack of teamwork in the preoperative "sign-in" phase doubled the chances of there being a lack of teamwork during surgery [odds ratio = 1.972, 95% confidence interval (CI) 1.741, 2.233, p < 0.001] and during the "time-out" phase (odds ratio = 2.142, 95% CI 1.879, 2.441, p < 0.001). Consistent presence of staff during surgery significantly increased teamwork, by 21% for physicians and 24% for nurses (p < 0.05), but staff turnover significantly decreased teamwork, by 73% for physicians (p < 0.05). Interview data indicated that patient safety and staff psychological safety are related to a perception of a collaborative team role among OR staff, with mutual commitment and effective interprofessional communication. Conclusions: Healthcare organizations should consider the key finding of this study when trying to identify factors that affect teamwork during a surgery. Effective preoperative teamwork positively affects intraoperative teamwork, as does the presence of more clinicians participating in a surgery, with no turnover. Other factors include working in a fixed, designated team, led by a surgeon, which functions with effective interprofessional communication that promotes patient safety and staff psychological safety.

Evaluating the Balance Between Privacy and Access in Digital Information Sharing.

OBJECTIVES: Access to personal health records in an ICU by persons involved in the patient's care (referred to broadly as "family members" below) has the potential to increase engagement and reduce the negative psychologic sequelae of such hospitalizations. Currently, little is known about patient preferences for information sharing with a designated family member in the ICU. We sought to understand the information-sharing preferences of former ICU patients and their family members and to identify predictors of information-sharing preferences. DESIGN: We performed an internet survey that was developed by a broad, multidisciplinary team of stakeholders. Formal pilot testing of the survey was conducted prior to internet survey administration to study subjects. SETTING: Internet survey. SUBJECTS: Subjects included English-speaking adults who had an ICU experience or a family member with ICU experience between 2013 and 2016. We used panel sampling to ensure an ethnically representative sample of the U.S. population. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One thousand five hundred twenty surveys were submitted, and 1,470 were included in analysis. The majority of respondents (93.6%) stated that they would want to share present and past medical history, either all or that related to their ICU stay, with a designated family member of their choosing. The majority (79%) would also want their designated family member to be able to access that information from a home computer. Although most respondents preferred to share all types of information, they indicated varying levels of willingness to share specific types of more sensitive information. Information-sharing preferences did not differ by age, sex, ethnicity, or type of prior experience in the ICU (i.e., patient or family member). CONCLUSIONS: In the context of an ICU admission, sharing personal health information with a person of the patient's choosing appears desirable for most patients and family members. Policies and implementation of regulations should take this into consideration.

Understanding Perceived Appreciation to Create a Culture of Wellness.

OBJECTIVE: To fully address physician burnout, academic medical centers need cultures that promote well-being. One observed driver of a culture of wellness is perceived appreciation. The authors identified several contributors to perceived appreciation among faculty at a large, metropolitan academic institution through use of a novel survey. METHODS: The authors surveyed clinical faculty in five departments: psychiatry, emergency medicine, internal medicine, thoracic surgery, and radiology. Two open-ended response questions assessed sources of perceived and lack of perceived appreciation in narrative form. The authors also collected data on gender and department identity. Grounded theory methodology was used to analyze the narrative responses and design thinking to brainstorm specific recommendations based on the main themes identified. RESULTS: A total of 179 faculty respondents filled out the survey for an overall response rate of 29%. Major drivers of perceived appreciation were patient and families (42%); physician, trainee and non-physician colleagues (32.7%); chairs (10%); and compensation (3.3%). Major drivers of perceived lack of appreciation were disrespect for time and skill level, including inadequate staffing (30%); devaluation by a physician colleague, chief of one's service or the chair (29%); poor communication and transparency (13%); and patient and family anger (6%). CONCLUSIONS: Opportunities to improve perceived appreciation include structured communication of patient gratitude, community building programs, top of licensure initiatives and accountability for physician wellness, and inclusivity efforts from organizational leaders.

Contributors to Gender Differences in Burnout and Professional Fulfillment: A Survey of Physician Faculty.

BACKGROUND: This study was conducted to describe gender differences in physician burnout and professional fulfillment and to explore their potential contributors. METHODS: This was a single-center, cross-sectional survey study of physician faculty at Brigham and Women's Hospital, an academic medical center in Boston. The population included all physician faculty who practiced clinical medicine in 2017 (n = 2,388). The study was conducted using the Stanford Physician Wellness Survey. Burnout and professional fulfillment were the main outcome measures assessed. Other variables assessed included ratings of culture of wellness, personal resilience, and efficiency of practice factors associated with physician experience. RESULTS: The study population consisted of 1,066 faculty, of whom 46.4% were female and 59.8% were younger than 50. Female physicians reported significantly higher rates of burnout (42.4% vs. 34.4%, p = 0.01) and lower rates of professional fulfillment (35.1% vs. 50.4%, p < 0.01) than male physicians. Female physicians reported lower ratings for self-compassion and multiple culture of wellness factors. After adjusting for demographic factors and academic rank, the study team identified multiple culture of wellness factors (perceived appreciation, schedule control, work environment diversity and inclusion) and self-compassion as attenuating the significant relationship between gender and burnout. Only perceived appreciation attenuated the significant relationship between gender and professional fulfillment. CONCLUSION: This study demonstrated higher rates of burnout and lower rates of professional fulfillment among female vs. male physician faculty. Culture of wellness factors and self-compassion may contribute to gender differences in burnout and professional fulfillment and potentially represent modifiable targets for efforts seeking to eliminate gender disparities in physicians' workplace experiences.