Prevalence and Causes of Diagnostic Errors in Hospitalized Patients Under Investigation for COVID-19.

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.

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis.

INTRODUCTION: Accurate estimation of an expected discharge date (EDD) early during hospitalization impacts clinical operations and discharge planning. METHODS: We conducted a retrospective study of patients discharged from six general medicine units at an academic medical center in Boston, MA from January 2017 to June 2018. We retrieved all EDD entries and patient, encounter, unit, and provider data from the electronic health record (EHR), and public weather data. We excluded patients who expired, discharged against medical advice, or lacked an EDD within the first 24 h of hospitalization. We used generalized estimating equations in a multivariable logistic regression analysis to model early EDD accuracy (an accurate EDD entered within 24 h of admission), adjusting for all covariates and clustering by patient. We similarly constructed a secondary multivariable model using covariates present upon admission alone. RESULTS: Of 3917 eligible hospitalizations, 890 (22.7%) had at least one accurate early EDD entry. Factors significantly positively associated (OR > 1) with an accurate early EDD included clinician-entered EDD, admit day and discharge day during the work week, and teaching clinical units. Factors significantly negatively associated (OR < 1) with an accurate early EDD included Elixhauser Comorbidity Index ≥ 11 and length of stay of two or more days. C-statistics for the primary and secondary multivariable models were 0.75 and 0.60, respectively. CONCLUSIONS: EDDs entered within the first 24 h of admission were often inaccurate. While several variables from the EHR were associated with accurate early EDD entries, few would be useful for prospective prediction.

Toward an asthma patient-reported outcome measure for use in digital remote monitoring.

OBJECTIVE: To develop and test a patient-reported outcome measure (PROM) for suitability in digital remote asthma symptom monitoring to identify uncontrolled asthma. METHODS: We modified a 5-item PROM that does not require a license, the asthma control measure (ACM), from a one-month to one-week lookback period, and evaluated it using the 5-item asthma control questionnaire (ACQ-5). We recruited subjects with asthma through MTurk, an online platform. RESULTS: In a sample of 498 subjects, the ACM identified uncontrolled asthma with sensitivity 0.99 and specificity 0.65. The two measures correlated with r = 0.81. CONCLUSION: The ACM modified to a weekly lookback period can differentiate patients with well-controlled asthma from those with uncontrolled asthma. This PROM does not require a license and can be used in digital remote monitoring interventions.

Interactive Digital Health Tools to Engage Patients and Caregivers in Discharge Preparation: Implementation Study.

BACKGROUND: Poor discharge preparation during hospitalization may lead to adverse events after discharge. Checklists and videos that systematically engage patients in preparing for discharge have the potential to improve safety, especially when integrated into clinician workflow via the electronic health record (EHR). OBJECTIVE: This study aims to evaluate the implementation of a suite of digital health tools integrated with the EHR to engage hospitalized patients, caregivers, and their care team in preparing for discharge. METHODS: We used the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to identify pertinent research questions related to implementation. We iteratively refined patient and clinician-facing intervention components using a participatory process involving end users and institutional stakeholders. The intervention was implemented at a large academic medical center from December 2017 to July 2018. Patients who agreed to participate were coached to watch a discharge video, complete a checklist assessing discharge readiness, and request postdischarge text messaging with a physician 24 to 48 hours before their expected discharge date, which was displayed via a patient portal and bedside display. Clinicians could view concerns reported by patients based on their checklist responses in real time via a safety dashboard integrated with the EHR and choose to open a secure messaging thread with the patient for up to 7 days after discharge. We used mixed methods to evaluate our implementation experience. RESULTS: Of 752 patient admissions, 510 (67.8%) patients or caregivers participated: 416 (55.3%) watched the video and completed the checklist, and 94 (12.5%) completed the checklist alone. On average, 4.24 concerns were reported per each of the 510 checklist submissions, most commonly about medications (664/2164, 30.7%) and follow-up (656/2164, 30.3%). Of the 510 completed checklists, a member of the care team accessed the safety dashboard to view 210 (41.2%) patient-reported concerns. For 422 patient admissions where postdischarge messaging was available, 141 (33.4%) patients requested this service; of these, a physician initiated secure messaging for 3 (2.1%) discharges. Most patient survey participants perceived that the intervention promoted self-management and communication with their care team. Patient interview participants endorsed gaps in communication with their care team and thought that the video and checklist would be useful closer toward discharge. Clinicians participating in focus groups perceived the value for patients but suggested that low awareness and variable workflow regarding the intervention, lack of technical optimization, and inconsistent clinician leadership limited the use of clinician-facing components. CONCLUSIONS: A suite of EHR-integrated digital health tools to engage patients, caregivers, and clinicians in discharge preparation during hospitalization was feasible, acceptable, and valuable; however, important challenges were identified during implementation. We offer strategies to address implementation barriers and promote adoption of these tools. TRIAL REGISTRATION: ClinicalTrials.gov NCT03116074; https://clinicaltrials.gov/ct2/show/NCT03116074.

Acute Care Patient Portal Intervention: Portal Use and Patient Activation.

BACKGROUND: Patient-facing health information technology (HIT) tools, such as patient portals, are recognized as a potential mechanism to facilitate patient engagement and patient-centered care, yet the use of these tools remains limited in the hospital setting. Although research in this area is growing, it is unclear how the use of acute care patient portals might affect outcomes, such as patient activation. OBJECTIVE: The aim of this study was to describe the use of an acute care patient portal and investigate its association with patient and care partner activation in the hospital setting. METHODS: We implemented an acute care patient portal on 6 acute care units over an 18-month period. We investigated the characteristics of the users (patients and their care partners) of the patient portal, as well as their use of the portal. This included the number of visits to each page, the number of days used, the length of the user's access period, and the average percent of days used during the access period. Patient and care partner activation was assessed using the short form of the patient activation measure (PAM-13) and the caregiver patient activation measure (CG-PAM). Comparisons of the activation scores were performed using propensity weighting and robust weighted linear regression. RESULTS: Of the 2974 randomly sampled patients, 59.01% (1755/2974) agreed to use the acute care patient portal. Acute care patient portal enrollees were younger, less sick, less likely to have Medicare as their insurer, and more likely to use the Partners Healthcare enterprise ambulatory patient portal (Patient Gateway). The most used features of the acute care patient portal were the laboratory test results, care team information, and medication list. Most users accessed the portal between 1 to 4 days during their hospitalization, and the average number of days used (logged in at least once per day) was 1.8 days. On average, users accessed the portal 42.69% of the hospital days during which it was available. There was significant association with patient activation on the neurology service (P<.001) and medicine service (P=.01), after the introduction of HIT tools and the acute care patient portal, but not on the oncology service. CONCLUSIONS: Portal users most often accessed the portal to view their clinical information, though portal usage was limited to only the first few days of enrollment. We found an association between the use of the portal and HIT tools with improved levels of patient activation. These tools may help facilitate patient engagement and improve outcomes when fully utilized by patients and care partners. Future study should leverage usage metrics to describe portal use and assess the impact of HIT tools on specific outcome measures in the hospital setting.

The Impact of Automated Notification on Follow-up of Actionable Tests Pending at Discharge: a Cluster-Randomized Controlled Trial.

BACKGROUND: Follow-up of tests pending at discharge (TPADs) is poor. We previously demonstrated a twofold increase in awareness of any TPAD by attendings and primary care physicians (PCPs) using an automated email intervention OBJECTIVE: To determine whether automated notification improves documented follow-up for actionable TPADs DESIGN: Cluster-randomized controlled trial SUBJECTS: Attendings and PCPs caring for adult patients discharged from general medicine and cardiology services with at least one actionable TPAD between June 2011 and May 2012 INTERVENTION: An automated system that notifies discharging attendings and network PCPs of finalized TPADs by email MAIN MEASURES: The primary outcome was the proportion of actionable TPADs with documented action determined by independent physician review of the electronic health record (EHR). Secondary outcomes included documented acknowledgment, 30-day readmissions, and adjusted median days to documented follow-up. KEY RESULTS: Of the 3378 TPADs sampled, 253 (7.5%) were determined to be actionable by physician review. Of these, 150 (123 patients discharged by 53 attendings) and 103 (90 patients discharged by 44 attendings) were assigned to intervention and usual care groups, respectively, and underwent chart review. The proportion of actionable TPADs with documented action was 60.7 vs. 56.3% (p = 0.82) in the intervention vs. usual care groups, similar for documented acknowledgment. The proportion of patients with actionable TPADs readmitted within 30 days was 22.8 vs. 31.1% in the intervention vs. usual care groups (p = 0.24). The adjusted median days [95% CI] to documented action was 9 [6.2, 11.8] vs. 14 [10.2, 17.8] (p = 0.04) in the intervention vs. usual care groups, similar for documented acknowledgment. In sub-group analysis, the intervention had greater impact on documented action for patients with network PCPs compared with usual care (70 vs. 50%, p = 0.03). CONCLUSIONS: Automated notification of actionable TPADs shortened time to action but did not significantly improve documented follow-up, except for network-affiliated patients. The high proportion of actionable TPADs without any documented follow-up (~ 40%) represents an ongoing safety concern. CLINICAL TRIALS IDENTIFIER: NCT01153451.

A Case of Disseminated Histoplasmosis in a Patient with Rheumatoid Arthritis on Abatacept.

Biologic agents are effective treatments for rheumatoid arthritis but are associated with important risks, including severe infections. Tumor Necrosis Factor (TNF) α inhibitors are known to increase the risk of systemic fungal infections such as disseminated histoplasmosis. Abatacept is a biologic agent with a mechanism different from that of TNFα inhibitors: It suppresses cellular immunity by competing for the costimulatory signal on antigen-presenting cells. The risk of disseminated histoplasmosis for patients on abatacept is not known. We report a case of abatacept-associated disseminated histoplasmosis and review the known infectious complications of abatacept. While the safety of resuming biologic agents following treatment for disseminated histoplasmosis is also not known, abatacept is recommended over TNFα inhibitors for rheumatoid arthritis patients with a prior serious infection. We discuss the evidence supporting this recommendation and discuss alternative treatments for rheumatoid arthritis patients with a history of a serious infection.

Changes in medical errors after implementation of a handoff program.

BACKGROUND: Miscommunications are a leading cause of serious medical errors. Data from multicenter studies assessing programs designed to improve handoff of information about patient care are lacking. METHODS: We conducted a prospective intervention study of a resident handoff-improvement program in nine hospitals, measuring rates of medical errors, preventable adverse events, and miscommunications, as well as resident workflow. The intervention included a mnemonic to standardize oral and written handoffs, handoff and communication training, a faculty development and observation program, and a sustainability campaign. Error rates were measured through active surveillance. Handoffs were assessed by means of evaluation of printed handoff documents and audio recordings. Workflow was assessed through time-motion observations. The primary outcome had two components: medical errors and preventable adverse events. RESULTS: In 10,740 patient admissions, the medical-error rate decreased by 23% from the preintervention period to the postintervention period (24.5 vs. 18.8 per 100 admissions, P<0.001), and the rate of preventable adverse events decreased by 30% (4.7 vs. 3.3 events per 100 admissions, P<0.001). The rate of nonpreventable adverse events did not change significantly (3.0 and 2.8 events per 100 admissions, P=0.79). Site-level analyses showed significant error reductions at six of nine sites. Across sites, significant increases were observed in the inclusion of all prespecified key elements in written documents and oral communication during handoff (nine written and five oral elements; P<0.001 for all 14 comparisons). There were no significant changes from the preintervention period to the postintervention period in the duration of oral handoffs (2.4 and 2.5 minutes per patient, respectively; P=0.55) or in resident workflow, including patient-family contact and computer time. CONCLUSIONS: Implementation of the handoff program was associated with reductions in medical errors and in preventable adverse events and with improvements in communication, without a negative effect on workflow. (Funded by the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services, and others.).

Effect of Pharmacist Counseling Intervention on Health Care Utilization Following Hospital Discharge: A Randomized Control Trial.

BACKGROUND: Reduction in 30-day readmission rates following hospitalization for acute coronary syndrome (ACS) and acute decompensated heart failure (ADHF) is a national goal. OBJECTIVE: The aim of this study was to determine the effect of a tailored, pharmacist-delivered, health literacy intervention on unplanned health care utilization, including hospital readmission or emergency room (ER) visit, following discharge. DESIGN: Randomized, controlled trial with concealed allocation and blinded outcome assessors SETTING: Two tertiary care academic medical centers PARTICIPANTS: Adults hospitalized with a diagnosis of ACS and/or ADHF. INTERVENTION: Pharmacist-assisted medication reconciliation, inpatient pharmacist counseling, low-literacy adherence aids, and individualized telephone follow-up after discharge MAIN MEASURES: The primary outcome was time to first unplanned health care event, defined as hospital readmission or an ER visit within 30 days of discharge. Pre-specified analyses were conducted to evaluate the effects of the intervention by academic site, health literacy status (inadequate versus adequate), and cognition (impaired versus not impaired). Adjusted hazard ratios (aHR) and 95% confidence intervals (CI) are reported. KEY RESULTS: A total of 851 participants enrolled in the study at Vanderbilt University Hospital (VUH) and Brigham and Women's Hospital (BWH). The primary analysis showed no statistically significant effect on time to first unplanned hospital readmission or ER visit among patients who received interventions compared to controls (aHR = 1.04, 95% CI 0.78-1.39). There was an interaction of treatment effect by site (p = 0.04 for interaction); VUH aHR = 0.77, 95% CI 0.51-1.15; BWH aHR = 1.44 (95% CI 0.95-2.12). The intervention reduced early unplanned health care utilization among patients with inadequate health literacy (aHR 0.41, 95% CI 0.17-1.00). There was no difference in treatment effect by patient cognition. CONCLUSION: A tailored, pharmacist-delivered health literacy-sensitive intervention did not reduce post-discharge unplanned health care utilization overall. The intervention was effective among patients with inadequate health literacy, suggesting that targeted practice of pharmacist intervention in this population may be advantageous.

Four-year impact of an alert notification system on closed-loop communication of critical test results.

OBJECTIVE: One of the patient safety goals proposed by the Joint Commission urges hospitals to develop a policy for communicating critical test results and to measure adherence to that policy. We evaluated the impact of an alert notification system on policy adherence for communicating critical imaging test results to referring providers and assessed system adoption over the first 4 years after implementation. MATERIALS AND METHODS: This study was performed in a 753-bed academic medical center. The intervention, an automated alert notification system for critical results, was implemented in January 2010. The primary outcome was adherence to institutional policy for timely closed-loop communication of critical imaging results, and the secondary outcome was system adoption. Policy adherence was determined through manual review of a random sample of radiology reports from the first 4 years after the intervention (n = 37,604) compared with baseline outcomes 1 year before the intervention (n = 9430). Adoption was evaluated by quantifying the use of the system overall and the proportion of alerts that used noninterruptive communication as a percentage of all reports generated by 320 radiologists (n = 1,538,059). A statistical analysis of the trend at 6-month intervals over 4 years was performed using a chi-square trend test. RESULTS: Adherence to the policy increased from 91.3% before the intervention to 95.0% after the intervention (p < 0.0001). There was a ninefold increase in the critical results communicated via the system (chi-square trend test, p < 0.0001). During the first 4 years after the intervention, 41,445 alerts (41% of the total number of alerts) used the system's noninterruptive process for communicating less urgent critical results, which was substantially unchanged over the 4 years postintervention, thus reducing unnecessary paging interruptions. CONCLUSION: An automated alert notification system for communicating critical imaging results was successfully adopted and was associated with increased adherence to institutional policy for communicating critical test results and with reduced workflow interruptions.

The Association of Standardized Documentation of Serious Illness Conversations With Healthcare Utilization in Hospitalized Patients: A Propensity Score Matched Cohort Analysis.

Background: Serious Illness Conversations (SICs) conducted during hospitalization can lead to meaningful patient participation in the decision-making process affecting medical management. The aim of this study is to determine if standardized documentation of a SIC within an institutionally approved EHR module during hospitalization is associated with palliative care consultation, change in code status, hospice enrollment prior to discharge, and 90-day readmissions. Methods: We conducted retrospective analyses of hospital encounters of general medicine patients at a community teaching hospital affiliated with an academic medical center from October 2018 to August 2019. Encounters with standardized documentation of a SIC were identified and matched by propensity score to control encounters without a SIC in a ratio of 1:3. We used multivariable, paired logistic regression and Cox proportional-hazards modeling to assess key outcomes. Results: Of 6853 encounters (5143 patients), 59 (.86%) encounters (59 patients) had standardized documentation of a SIC, and 58 (.85%) were matched to 167 control encounters (167 patients). Encounters with standardized documentation of a SIC had greater odds of palliative care consultation (odds ratio [OR] 60.10, 95% confidence interval [CI] 12.45-290.08, P < .01), a documented code status change (OR 8.04, 95% CI 1.54-42.05, P = .01), and discharge with hospice services (OR 35.07, 95% CI 5.80-212.08, P < .01) compared to matched controls. There was no significant association with 90-day readmissions (adjusted hazard ratio [HR] .88, standard error [SE] .37, P = .73). Conclusions: Standardized documentation of a SIC during hospitalization is associated with palliative care consultation, change in code status, and hospice enrollment.

A Mixed Methods Analysis of Standardized Documentation of Serious Illness Conversations Within an Electronic Health Record Module During Hospitalization.

BACKGROUND: Analysis of documented Serious Illness Conversations (SICs) in the inpatient setting can help clinicians align management to address patient and caregiver needs. METHODS: We conducted a mixed methods analysis of the first instance of standardized documentation of a SIC within a structured module among hospitalized general medicine patients from 2018 to 2019. Percentage of documentations that included a description of patient or family understanding of the patient's medical condition and use of radio buttons to answer the "prognostic information shared," "hopes," and "worries" modules are reported. Using grounded theory approach, physicians analyzed free text entries to: "What is important to the patient/family?" and "Recommendations or next steps planned." RESULTS: Out of 5142 patients, 59 patients had a documented SIC. Patient or family understanding of the medical condition(s) was reported in 56 (95%). For "prognostic information shared," the most frequently selected radio buttons were: 49 (83%) incurable disease and 28 (48%) prognosis of weeks to months while those for "hopes" were: 52 (88%) be comfortable and 27 (46%) be at home and for "worries" were: 49 (83%) other physical suffering and 36 (61%) pain. Themes generated from entries to "What's important to patient/family?" included being with loved ones; comfort; mentally and physically present; and reliable care while those for "Recommendations" were coordinating support services; symptom management; and support and communication. CONCLUSIONS: SIC content indicated concern about pain and reliable care suggesting the complex, intensive nature of caring for seriously ill patients and the need to consider SICs earlier in the life course of patients.

Identification of Hospitalized Patients Who May Benefit from a Serious Illness Conversation Using the Readmission Risk Score Combined with the Surprise Question.

BACKGROUND: Determining which patients benefit from a serious illness conversation (SIC) is challenging. The authors sought to determine whether Epic's Risk of Readmission Score (RRS), could be combined with a simple, validated, one-question mortality prognostic screen (the surprise question: Would you be surprised if the patient died in the next 12 months?) to identify hospitalized patients with SIC needs. METHODS: In this retrospective study, the authors randomly selected encounters for patients ≥ 18 years of age to a general medicine service from January 2019 to October 2021 who had an RRS > 28%. Two adjudicators independently performed chart reviews for each encounter to answer the surprise question to create two distinct prognostic groups (yes vs. no). Fisher's exact test was used to assess for statistically significant differences in standardized documentation of SICs between groups. RESULTS: Out of 2,879 encounters, 202 patient encounters were randomly selected. Adjudicators answered "no" to the surprise question for 156 (77.2%) patients. Patients for whom adjudicators answered "no" were generally older with higher comorbidity and more often had standardized documentation of a SIC (14 [9.0%] vs. 0.[0.0%], p = 0.042) compared to patients for whom adjudicators answered "yes." CONCLUSION: Approximately three quarters of patients with a high RRS were predicted to have a lifespan of less than a year. Although these patients were significantly more likely to have a SIC, rates of SICs were extremely low. Combining available electronic health record (EHR) data with a simple one-question screening tool may help identify hospitalized patients who require a SIC in quality improvement initiatives.

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.

Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care.

Importance: Diagnostic errors contribute to patient harm, though few data exist to describe their prevalence or underlying causes among medical inpatients. Objective: To determine the prevalence, underlying cause, and harms of diagnostic errors among hospitalized adults transferred to an intensive care unit (ICU) or who died. Design, Setting, and Participants: Retrospective cohort study conducted at 29 academic medical centers in the US in a random sample of adults hospitalized with general medical conditions and who were transferred to an ICU, died, or both from January 1 to December 31, 2019. Each record was reviewed by 2 trained clinicians to determine whether a diagnostic error occurred (ie, missed or delayed diagnosis), identify diagnostic process faults, and classify harms. Multivariable models estimated association between process faults and diagnostic error. Opportunity for diagnostic error reduction associated with each fault was estimated using the adjusted proportion attributable fraction (aPAF). Data analysis was performed from April through September 2023. Main Outcomes and Measures: Whether or not a diagnostic error took place, the frequency of underlying causes of errors, and harms associated with those errors. Results: Of 2428 patient records at 29 hospitals that underwent review (mean [SD] patient age, 63.9 [17.0] years; 1107 [45.6%] female and 1321 male individuals [54.4%]), 550 patients (23.0%; 95% CI, 20.9%-25.3%) had experienced a diagnostic error. Errors were judged to have contributed to temporary harm, permanent harm, or death in 436 patients (17.8%; 95% CI, 15.9%-19.8%); among the 1863 patients who died, diagnostic error was judged to have contributed to death in 121 (6.6%; 95% CI, 5.3%-8.2%). In multivariable models examining process faults associated with any diagnostic error, patient assessment problems (aPAF, 21.4%; 95% CI, 16.4%-26.4%) and problems with test ordering and interpretation (aPAF, 19.9%; 95% CI, 14.7%-25.1%) had the highest opportunity to reduce diagnostic errors; similar ranking was seen in multivariable models examining harmful diagnostic errors. Conclusions and Relevance: In this cohort study, diagnostic errors in hospitalized adults who died or were transferred to the ICU were common and associated with patient harm. Problems with choosing and interpreting tests and the processes involved with clinician assessment are high-priority areas for improvement efforts.

Effect of digital tools to promote hospital quality and safety on adverse events after discharge.

OBJECTIVES: Post-discharge adverse events (AEs) are common and heralded by new and worsening symptoms (NWS). We evaluated the effect of electronic health record (EHR)-integrated digital tools designed to promote quality and safety in hospitalized patients on NWS and AEs after discharge. MATERIALS AND METHODS: Adult general medicine patients at a community hospital were enrolled. We implemented a dashboard which clinicians used to assess safety risks during interdisciplinary rounds. Post-implementation patients were randomized to complete a discharge checklist whose responses were incorporated into the dashboard. Outcomes were assessed using EHR review and 30-day call data adjudicated by 2 clinicians and analyzed using Poisson regression. We conducted comparisons of each exposure on post-discharge outcomes and used selected variables and NWS as independent predictors to model post-discharge AEs using multivariable logistic regression. RESULTS: A total of 260 patients (122 pre, 71 post [dashboard], 67 post [dashboard plus discharge checklist]) enrolled. The adjusted incidence rate ratios (aIRR) for NWS and AEs were unchanged in the post- compared to pre-implementation period. For patient-reported NWS, aIRR was non-significantly higher for dashboard plus discharge checklist compared to dashboard participants (1.23 [0.97,1.56], P = .08). For post-implementation patients with an AE, aIRR for duration of injury (>1 week) was significantly lower for dashboard plus discharge checklist compared to dashboard participants (0 [0,0.53], P < .01). In multivariable models, certain patient-reported NWS were associated with AEs (3.76 [1.89,7.82], P < .01). DISCUSSION: While significant reductions in post-discharge AEs were not observed, checklist participants experiencing a post-discharge AE were more likely to report NWS and had a shorter duration of injury. CONCLUSION: Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge. CLINICALTRIALS.GOV: NCT05232656.

Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial.

BACKGROUND: Clinically important medication errors are common after hospital discharge. They include preventable or ameliorable adverse drug events (ADEs), as well as medication discrepancies or nonadherence with high potential for future harm (potential ADEs). OBJECTIVE: To determine the effect of a tailored intervention on the occurrence of clinically important medication errors after hospital discharge. DESIGN: Randomized, controlled trial with concealed allocation and blinded outcome assessors. (ClinicalTrials.gov registration number: NCT00632021) SETTING: Two tertiary care academic hospitals. PATIENTS: Adults hospitalized with acute coronary syndromes or acute decompensated heart failure. INTERVENTION: Pharmacist-assisted medication reconciliation, inpatient pharmacist counseling, low-literacy adherence aids, and individualized telephone follow-up after discharge. MEASUREMENTS: The primary outcome was the number of clinically important medication errors per patient during the first 30 days after hospital discharge. Secondary outcomes included preventable or ameliorable ADEs, as well as potential ADEs. RESULTS: Among 851 participants, 432 (50.8%) had 1 or more clinically important medication errors; 22.9% of such errors were judged to be serious and 1.8% life-threatening. Adverse drug events occurred in 258 patients (30.3%) and potential ADEs in 253 patients (29.7%). The intervention did not significantly alter the per-patient number of clinically important medication errors (unadjusted incidence rate ratio, 0.92 [95% CI, 0.77 to 1.10]) or ADEs (unadjusted incidence rate ratio, 1.09 [CI, 0.86 to 1.39]). Patients in the intervention group tended to have fewer potential ADEs (unadjusted incidence rate ratio, 0.80 [CI, 0.61 to 1.04]). LIMITATION: The characteristics of the study hospitals and participants may limit generalizability. CONCLUSION: Clinically important medication errors were present among one half of patients after hospital discharge and were not significantly reduced by a health-literacy-sensitive, pharmacist-delivered intervention. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.

Who Gets (and Who Should Get) a Serious Illness Conversation in the Hospital? An Analysis of Readmission Risk Score in an Electronic Health Record.

Serious Illness Conversations (SICs) explore patients' prognostic awareness, hopes, and worries, and can help establish priorities for their care during and after hospitalization. While identifying patients who benefit from an SIC remains a challenge, this task may be facilitated by use of validated prediction scores available in most commercial electronic health records (EHRs), such as Epic's Readmission Risk Score (RRS). We identified the RRS on admission for all hospital encounters from October 2018 to August 2019 and measured the area under the receiver operating characteristic (AUROC) curve to determine whether RRS could accurately discriminate post discharge 6-month mortality. For encounters with standardized SIC documentation matched in a 1:3 ratio to controls by sex and age (±5 years), we constructed a multivariable, paired logistic regression model and measured the odds of SIC documentation per every 10% absolute increase in RRS. RRS was predictive of 6-month mortality with acceptable discrimination (AUROC .71) and was significantly associated with SIC documentation (adjusted OR 1.42, 95% CI 1.24-1.63). An RRS >28% used to identify patients with post discharge 6-month mortality had a high specificity (89.0%) and negative predictive value (NPV) (97.0%), but low sensitivity (25.2%) and positive predictive value (PPV) (7.9%). RRS may serve as a practical EHR-based screen to exclude patients not requiring an SIC, thereby leaving a smaller cohort to be further evaluated for SIC needs using other validated tools and clinical assessment.

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.

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.