Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

The Safety of Inpatient Health Care.

BACKGROUND: Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted. METHODS: We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events. RESULTS: In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%). CONCLUSIONS: Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.).

The Safety of Outpatient Health Care : Review of Electronic Health Records.

BACKGROUND: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. OBJECTIVE: To measure AEs in the outpatient setting. DESIGN: Retrospective review of the electronic health record (EHR). SETTING: 11 outpatient sites in Massachusetts in 2018. PATIENTS: 3103 patients who received outpatient care. MEASUREMENTS: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. RESULTS: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care-associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, -0.05 [CI, -0.09 to -0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. LIMITATION: Retrospective EHR review may miss AEs. CONCLUSION: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. PRIMARY FUNDING SOURCE: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.

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.

A SARS-CoV-2 Cluster in an Acute Care Hospital.

BACKGROUND: Little is known about clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in acute care hospitals. OBJECTIVE: To describe the detection, mitigation, and analysis of a large cluster of SARS-CoV-2 infections in an acute care hospital with mature infection control policies. DESIGN: Descriptive study. SETTING: Brigham and Women's Hospital, Boston, Massachusetts. PARTICIPANTS: Patients and staff with cluster-related SARS-CoV-2 infections. INTERVENTION: Close contacts of infected patients and staff were identified and tested every 3 days, patients on affected units were preemptively isolated and repeatedly tested, affected units were cleaned, room ventilation was measured, and specimens were sent for whole-genome sequencing. A case-control study was done to compare clinical interactions, personal protective equipment use, and breakroom and workroom practices in SARS-CoV-2-positive versus negative staff. MEASUREMENTS: Description of the cluster, mitigation activities, and risk factor analysis. RESULTS: Fourteen patients and 38 staff members were included in the cluster per whole-genome sequencing and epidemiologic associations. The index case was a symptomatic patient in whom isolation was discontinued after 2 negative results on nasopharyngeal polymerase chain reaction testing. The patient subsequently infected multiple roommates and staff, who then infected others. Seven of 52 (13%) secondary infections were detected only on second or subsequent tests. Eight of 9 (89%) patients who shared rooms with potentially contagious patients became infected. Potential contributing factors included high viral loads, nebulization, and positive pressure in the index patient's room. Risk factors for transmission to staff included presence during nebulization, caring for patients with dyspnea or cough, lack of eye protection, at least 15 minutes of exposure to case patients, and interactions with SARS-CoV-2-positive staff in clinical areas. Whole-genome sequencing confirmed that 2 staff members were infected despite wearing surgical masks and eye protection. LIMITATION: Findings may not be generalizable. CONCLUSION: SARS-CoV-2 clusters can occur in hospitals despite robust infection control policies. Insights from this cluster may inform additional measures to protect patients and staff. PRIMARY FUNDING SOURCE: None.

The glaring omission in hospital ranking and quality grading programs: emergency department boarding.

Hospitals in the United States are assessed and ranked by several agencies and services, including U.S. News & World Report. Frequently, though, the key hospital throughput metric of inpatient boarding time in the emergency department (ED) is not considered when ranking hospitals. As a result, there is a discordance in which highly ranking hospitals may be poor performers in boarding of patients, a practice with known adverse safety effects. This article outlines the rationale for considering ED boarding in hospital ranking and quality assessments.

Does Addition of Intravenous Metronidazole to Oral Vancomycin Improve Outcomes in Clostridioides difficile Infection?

BACKGROUND: Guidelines recommend adding intravenous (IV) metronidazole to oral vancomycin for fulminant Clostridioides difficile infection (CDI). In this study, we compared dual therapy with IV metronidazole and vancomycin vs vancomycin monotherapy. We assessed prevalence of use and effectiveness of dual therapy in nonfulminant and fulminant CDI. METHODS: This was a 2-center retrospective study conducted from 2010 to 2018. Adult inpatients were included if they had a positive C. difficile polymerase chain reaction (PCR) performed on an unformed stool and received vancomycin within 2 days of testing. Patients were classified as having received dual therapy if IV metronidazole was given within the same time window, and otherwise classified as vancomycin monotherapy. The primary outcome was death or colectomy within 90 days after the index test. Logistic regression modeling was used to adjust for CDI severity and other established predictors of CDI outcomes. CDI recurrence was examined as a secondary outcome, adjusting for death as a competing risk. RESULTS: The study included 2114 patients (dual therapy, 993; monotherapy, 1121); 23% met the primary outcome. There was no association between dual therapy and the primary outcome (adjusted odds ratio [aOR], 1.07; 95% confidence interval [CI], .79-1.45), which remained true when the analysis was restricted to patients with fulminant CDI (aOR, 1.17; 95% CI, .65-2.10). There was also no association between dual therapy and CDI recurrence. CONCLUSIONS: Dual therapy with IV metronidazole and vancomycin was common for nonfulminant and fulminant CDI but was not associated with improved outcomes compared with vancomycin alone.

An Internet of Things Buttons to Measure and Respond to Restroom Cleanliness in a Hospital Setting: Descriptive Study.

BACKGROUND: Restroom cleanliness is an important factor in hospital quality. Due to its dynamic process, it can be difficult to detect the presence of dirty restrooms that need to be cleaned. Using an Internet of Things (IoT) button can permit users to designate restrooms that need cleaning and in turn, allow prompt response from housekeeping to maintain real-time restroom cleanliness. OBJECTIVE: This study aimed to describe the deployment of an IoT button-based notification system to measure hospital restroom cleanliness reporting system usage and qualitative feedback from housekeeping staff on IoT button use. METHODS: We deployed IoT buttons in 16 hospital restrooms. Over an 8-month period, housekeeping staff received real-time notifications and responded to button presses for restroom cleaning. All button presses were recorded. We reported average button usage by hospital area, time of day, and day of week. We also conducted interviews with housekeeping supervisors and staff to understand their acceptance of and experience with the system. RESULTS: Over 8 months, 1920 requests to clean restrooms in the main hospital lobby and satellite buildings were received. The hospital lobby IoT buttons received over half (N=1055, 55%) of requests for cleaning. Most requests occurred in afternoon hours from 3 PM to midnight. Requests for cleaning remained stable throughout the work week with fewer requests occurring over weekends. IoT button use was sustained throughout the study period. Interviews with housekeeping supervisors and staff demonstrated acceptance of the IoT buttons; actual use was centered around asynchronous communication between supervisors and staff in response to requests to clean restrooms. CONCLUSIONS: An IoT button system is a feasible method to generate on-demand request for restroom cleaning that is easy to deploy and that users will consistently engage with. Data from this system have the potential to enable responsive scheduling for restroom service and anticipate periods of high restroom utilization in a hospital.

Effect of Restriction of the Number of Concurrently Open Records in an Electronic Health Record on Wrong-Patient Order Errors: A Randomized Clinical Trial.

Importance: Recommendations in the United States suggest limiting the number of patient records displayed in an electronic health record (EHR) to 1 at a time, although little evidence supports this recommendation. Objective: To assess the risk of wrong-patient orders in an EHR configuration limiting clinicians to 1 record vs allowing up to 4 records opened concurrently. Design, Setting, and Participants: This randomized clinical trial included 3356 clinicians at a large health system in New York and was conducted from October 2015 to April 2017 in emergency department, inpatient, and outpatient settings. Interventions: Clinicians were randomly assigned in a 1:1 ratio to an EHR configuration limiting to 1 patient record open at a time (restricted; n = 1669) or allowing up to 4 records open concurrently (unrestricted; n = 1687). Main Outcomes and Measures: The unit of analysis was the order session, a series of orders placed by a clinician for a single patient. The primary outcome was order sessions that included 1 or more wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder measure (an electronic query that identifies orders placed for a patient, retracted, and then reordered shortly thereafter by the same clinician for a different patient). Results: Among the 3356 clinicians who were randomized (mean [SD] age, 43.1 [12.5] years; mean [SD] experience at study site, 6.5 [6.0] years; 1894 females [56.4%]), all provided order data and were included in the analysis. The study included 12 140 298 orders, in 4 486 631 order sessions, placed for 543 490 patients. There was no significant difference in wrong-patient order sessions per 100 000 in the restricted vs unrestricted group, respectively, overall (90.7 vs 88.0; odds ratio [OR], 1.03 [95% CI, 0.90-1.20]; P = .60) or in any setting (ED: 157.8 vs 161.3, OR, 1.00 [95% CI, 0.83-1.20], P = .96; inpatient: 185.6 vs 185.1, OR, 0.99 [95% CI, 0.89-1.11]; P = .86; or outpatient: 7.9 vs 8.2, OR, 0.94 [95% CI, 0.70-1.28], P = .71). The effect did not differ among settings (P for interaction = .99). In the unrestricted group overall, 66.2% of the order sessions were completed with 1 record open, including 34.5% of ED, 53.7% of inpatient, and 83.4% of outpatient order sessions. Conclusions and Relevance: A strategy that limited clinicians to 1 EHR patient record open compared with a strategy that allowed up to 4 records open concurrently did not reduce the proportion of wrong-patient order errors. However, clinicians in the unrestricted group placed most orders with a single record open, limiting the power of the study to determine whether reducing the number of records open when placing orders reduces the risk of wrong-patient order errors. Trial Registration: clinicaltrials.gov Identifier: NCT02876588.

Prophylaxis for Stress Ulcers With Proton Pump Inhibitors Is Not Associated With Increased Risk of Bloodstream Infections in the Intensive Care Unit.

BACKGROUND & AIMS: Proton pump inhibitors (PPIs) have been associated with increased risk of infection, likely because of changes in intestinal epithelial permeability and the gastrointestinal microbiome. PPIs are frequently given to patients in the intensive care unit (ICU) to prevent stress ulcers. These patients are at risk for bloodstream infections (BSIs), so we investigated the relationship between PPI use and BSIs among patients in the ICU. METHODS: We performed a retrospective cohort study of adults (≥18 years) admitted to 1 of 14 ICUs within a hospital network of 3 large hospitals from 2008 through 2014. The primary exposure was PPI use for stress ulcer prophylaxis in the ICU. The primary outcome was BSI, confirmed by culture analysis, arising 48 hours or more after admission to the ICU. Subjects were followed for 30 days after ICU admission or until death, discharge, or BSI. Multivariable Cox proportional hazards modeling was used to test the association between PPIs and BSI after controlling for patient comorbidities and other clinical factors. RESULTS: We analyzed data from 24,774 patients in the ICU, including 756 patients (3.1%) who developed BSIs while in the ICU. The cumulative incidence of BSI was 3.7% in patients with PPI exposure compared with 2.2% in patients without PPI exposure (log-rank test, P < .01). After adjusting for potential confounders, PPI exposure was not associated with increased risk of BSI while in the ICU (adjusted hazard ratio, 1.08; 95% confidence interval, 0.91-1.29). Comorbidities, antibiotic use, and mechanical ventilation were all independently associated with increased risk for BSIs. CONCLUSIONS: In a retrospective study of patients in the ICU, administration of PPIs to prevent bleeding was not associated with increased risk of BSI. These findings indicate that concern for BSI should not affect decisions regarding use of PPIs in the ICU.

Proton Pump Inhibitors Do Not Increase Risk for Clostridium difficile Infection in the Intensive Care Unit.

OBJECTIVES: Patients in the intensive care unit (ICU) frequently receive proton pump inhibitors (PPIs) and have high rates of Clostridium difficile infection (CDI). PPIs have been associated with CDI in hospitalized patients, but ICU patients differ fundamentally from non-ICU patients and few studies have focused on PPI use exclusively in the critical care setting. We performed a retrospective cohort study to determine the associations between PPIs and health-care facility-onset CDI in the ICU. METHODS: We analyzed data from all adult ICU patients at three affiliated hospitals (14 ICUs) between 2010 and 2013. Patients were excluded if they had recent CDI or an ICU stay of <3 days. We parsed electronic medical records for ICU exposures, focusing on PPIs and other potentially modifiable exposures that occurred during ICU stays. Health-care facility-onset CDI in the ICU was defined as a newly positive PCR for the C. difficile toxin B gene from an unformed stool, with subsequent receipt of anti-CDI therapy. We analyzed PPIs and other exposures as time-varying covariates and used Cox proportional hazards models to adjust for demographics, comorbidities, and other clinical factors. RESULTS: Of 18,134 patients who met the criteria for inclusion, 271 (1.5%) developed health-care facility-onset CDI in the ICU. Receipt of antibiotics was the strongest risk factor for CDI (adjusted HR (aHR) 2.79; 95% confidence interval (CI), 1.50-5.19). There was no significant increase in risk for CDI associated with PPIs in those who did not receive antibiotics (aHR 1.56; 95% CI, 0.72-3.35), and PPIs were actually associated with a decreased risk for CDI in those who received antibiotics (aHR 0.64; 95% CI, 0.48-0.83). There was also no evidence of increased risk for CDI in those who received higher doses of PPIs. CONCLUSIONS: Exposure to antibiotics was the most important risk factor for health-care facility-onset CDI in the ICU. PPIs did not increase risk for CDI in the ICU regardless of use of antibiotics.

Intercepting wrong-patient orders in a computerized provider order entry system.

STUDY OBJECTIVE: We evaluate the short- and long-term effect of a computerized provider order entry-based patient verification intervention to reduce wrong-patient orders in 5 emergency departments. METHODS: A patient verification dialog appeared at the beginning of each ordering session, requiring providers to confirm the patient's identity after a mandatory 2.5-second delay. Using the retract-and-reorder technique, we estimated the rate of wrong-patient orders before and after the implementation of the intervention to intercept these errors. We conducted a short- and long-term quasi-experimental study with both historical and parallel controls. We also measured the amount of time providers spent addressing the verification system, and reasons for discontinuing ordering sessions as a result of the intervention. RESULTS: Wrong-patient orders were reduced by 30% immediately after implementation of the intervention. This reduction persisted when inpatients were used as a parallel control. After 2 years, the rate of wrong-patient orders remained 24.8% less than before intervention. The mean viewing time of the patient verification dialog was 4.2 seconds (SD=4.0 seconds) and was longer when providers indicated they placed the order for the wrong patient (4.9 versus 4.1 seconds). Although the display of each dialog took only seconds, the large number of display episodes triggered meant that the physician time to prevent each retract-and-reorder event was 1.5 hours. CONCLUSION: A computerized provider order entry-based patient verification system led to a moderate reduction in wrong-patient orders that was sustained over time. Interception of wrong-patient orders at data entry is an important step in reducing these errors.

Retrospective cohort study of wrong-patient imaging order errors: how many reach the patient?

Studying near-miss errors is essential to preventing errors from reaching patients. When an error is committed, it may be intercepted (near-miss) or it will reach the patient; estimates of the proportion that reach the patient vary widely. To better understand this relationship, we conducted a retrospective cohort study using two objective measures to identify wrong-patient imaging order errors involving radiation, estimating the proportion of errors that are intercepted and those that reach the patient. This study was conducted at a large integrated healthcare system using data from 1 January to 31 December 2019. The study used two outcome measures of wrong-patient orders: (1) wrong-patient orders that led to misadministration of radiation reported to the New York Patient Occurrence Reporting and Tracking System (NYPORTS) (misadministration events); and (2) wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder (RAR) measure, a measure identifying orders placed for a patient, retracted and rapidly reordered by the same clinician on a different patient (near-miss events). All imaging orders that involved radiation were extracted retrospectively from the healthcare system data warehouse. Among 293 039 total eligible orders, 151 were wrong-patient orders (3 misadministration events, 148 near-miss events), for an overall rate of 51.5 per 100 000 imaging orders involving radiation placed on the wrong patient. Of all wrong-patient imaging order errors, 2% reached the patient, translating to 50 near-miss events for every 1 error that reached the patient. This proportion provides a more accurate and reliable estimate and reinforces the utility of systematic measure of near-miss errors as an outcome for preventative interventions.

Clinical Decision Support for Hypertension Management in Chronic Kidney Disease: A Randomized Clinical Trial.

Importance: Chronic kidney disease (CKD) affects 37 million adults in the United States, and for patients with CKD, hypertension is a key risk factor for adverse outcomes, such as kidney failure, cardiovascular events, and death. Objective: To evaluate a computerized clinical decision support (CDS) system for the management of uncontrolled hypertension in patients with CKD. Design, Setting, and Participants: This multiclinic, randomized clinical trial randomized primary care practitioners (PCPs) at a primary care network, including 15 hospital-based, ambulatory, and community health center-based clinics, through a stratified, matched-pair randomization approach February 2021 to February 2022. All adult patients with a visit to a PCP in the last 2 years were eligible and those with evidence of CKD and hypertension were included. Intervention: The intervention consisted of a CDS system based on behavioral economic principles and human-centered design methods that delivered tailored, evidence-based recommendations, including initiation or titration of renin-angiotensin-aldosterone system inhibitors. The patients in the control group received usual care from PCPs with the CDS system operating in silent mode. Main Outcomes and Measures: The primary outcome was the change in mean systolic blood pressure (SBP) between baseline and 180 days compared between groups. The primary analysis was a repeated measures linear mixed model, using SBP at baseline, 90 days, and 180 days in an intention-to-treat repeated measures model to account for missing data. Secondary outcomes included blood pressure (BP) control and outcomes such as percentage of patients who received an action that aligned with the CDS recommendations. Results: The study included 174 PCPs and 2026 patients (mean [SD] age, 75.3 [0.3] years; 1223 [60.4%] female; mean [SD] SBP at baseline, 154.0 [14.3] mm Hg), with 87 PCPs and 1029 patients randomized to the intervention and 87 PCPs and 997 patients randomized to usual care. Overall, 1714 patients (84.6%) were treated for hypertension at baseline. There were 1623 patients (80.1%) with an SBP measurement at 180 days. From the linear mixed model, there was a statistically significant difference in mean SBP change in the intervention group compared with the usual care group (change, -14.6 [95% CI, -13.1 to -16.0] mm Hg vs -11.7 [-10.2 to -13.1] mm Hg; P = .005). There was no difference in the percentage of patients who achieved BP control in the intervention group compared with the control group (50.4% [95% CI, 46.5% to 54.3%] vs 47.1% [95% CI, 43.3% to 51.0%]). More patients received an action aligned with the CDS recommendations in the intervention group than in the usual care group (49.9% [95% CI, 45.1% to 54.8%] vs 34.6% [95% CI, 29.8% to 39.4%]; P < .001). Conclusions and Relevance: These findings suggest that implementing this computerized CDS system could lead to improved management of uncontrolled hypertension and potentially improved clinical outcomes at the population level for patients with CKD. Trial Registration: ClinicalTrials.gov Identifier: NCT03679247.

Proton pump inhibitors and risk for recurrent Clostridium difficile infection among inpatients.

OBJECTIVES: Observational studies suggest that proton pump inhibitors (PPIs) are a risk factor for incident Clostridium difficile infection (CDI). Data also suggest an association between PPIs and recurrent CDI, although large-scale studies focusing solely on hospitalized patients are lacking. We therefore performed a retrospective cohort analysis of inpatients with incident CDI to assess receipt of PPIs as a risk factor for CDI recurrence in this population. METHODS: Using electronic medical records, we identified hospitalized adult patients between 1 December 2009 and 30 June 2012 with incident CDI, defined as a first positive stool test for C. difficile toxin B and who received appropriate treatment. Electronic records were parsed for clinical factors including receipt of PPIs, other acid suppression, non-CDI antibiotics, and comorbidities. The primary exposure was in-hospital PPIs given concurrently with C. difficile treatment. Recurrence was defined as a second positive stool test 15-90 days after the initial positive test. C. difficile recurrence rates in the PPI exposed and unexposed groups were compared with the log-rank test. Multivariable Cox proportional hazards modeling was performed to control for demographics, comorbidities, and other clinical factors. RESULTS: We identified 894 inpatients with incident CDI. The cumulative incidence of CDI recurrence in the cohort was 23%. Receipt of PPIs concurrent with CDI treatment was not associated with C. difficile recurrence (hazard ratio (HR)=0.82; 95% confidence interval (CI)=0.58-1.16). Black race (HR=1.66, 95% CI=1.05-2.63), increased age (HR=1.02, 95% CI=1.01-1.03), and increased comorbidities (HR=1.09, 95% CI=1.04-1.14) were associated with CDI recurrence. In light of a higher 90-day mortality seen among those who received PPIs (log-rank P=0.02), we also analyzed the subset of patients who survived to 90 days of follow-up. Again, there was no association between PPIs and CDI recurrence (HR=0.87; 95% CI=0.60-1.28). Finally, there was no association between recurrent CDI and increased duration or dose of PPIs. CONCLUSIONS: Among hospitalized adults with C. difficile, receipt of PPIs concurrent with C. difficile treatment was not associated with CDI recurrence. Black race, increased age, and increased comorbidities significantly predicted recurrence. Future studies should test interventions to prevent CDI recurrence among high-risk inpatients.

An automated tool for detecting medication overuse based on the electronic health records.

PURPOSE: Medication overuse is a serious concern in healthcare as it leads to increased expenditures, side effects, and morbidities. Identifying overuse is only possible through excluding appropriate indications that are primarily mentioned in unstructured notes. We developed a framework for automatic identification of medication overuse and applied it to proton pump inhibitors (PPIs). METHODS: We first created an indications knowledge base using data from drug labels, clinical guidelines, expert opinion, and other sources. We also obtained the list of current problems for 200 randomly selected inpatients who received PPIs using a natural language processing system and the discharge summaries of those patients. These problems were checked against the indications knowledge base to identify overuse candidates. Two gastroenterologists manually reviewed the notes and identified cases of overuse. Results from the automated framework were compared with the manual review. RESULTS: Reviewers had high interrater reliability in finding indications (agreement = 92.1%, Cohen's κ = 0.773). In 137 notes included in the final analysis, our system identified indications with a sensitivity of 74% (95%CI = 59-86) and specificity of 95% (95%CI = 87-98). In cases of appropriate use where the automated system also found one or more indications, it always included the correct indication. CONCLUSIONS: We created an automated system that can identify established indications of medication use in electronic health records with high accuracy. It can provide clinical decision support for identifying potential overuse of PPIs and could be useful for reducing overuse and encouraging better documentation of indications.

Effect of restricting electronic health records on clinician efficiency: substudy of a randomized clinical trial.

A prior randomized controlled trial (RCT) showed no significant difference in wrong-patient errors between clinicians assigned to a restricted electronic health record (EHR) configuration (limiting to 1 record open at a time) versus an unrestricted EHR configuration (allowing up to 4 records open concurrently). However, it is unknown whether an unrestricted EHR configuration is more efficient. This substudy of the RCT compared clinician efficiency between EHR configurations using objective measures. All clinicians who logged onto the EHR during the substudy period were included. The primary outcome measure of efficiency was total active minutes per day. Counts were extracted from audit log data, and mixed-effects negative binomial regression was performed to determine differences between randomized groups. Incidence rate ratios (IRRs) were calculated with 95% confidence intervals (CIs). Among a total of 2556 clinicians, there was no significant difference between unrestricted and restricted groups in total active minutes per day (115.1 vs 113.3 min, respectively; IRR, 0.99; 95% CI, 0.93-1.06), overall or by clinician type and practice area.

Forecasting hospital-level COVID-19 admissions using real-time mobility data.

BACKGROUND: For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. METHODS: Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. RESULTS: Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. CONCLUSIONS: The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.

During the COVID-19 pandemic, hospitals have needed to make challenging decisions around staffing and preparedness based on estimates of the number of admissions multiple weeks ahead. Forecasting techniques using methods from machine learning have been successfully applied to predict hospital admissions statewide, but the ability to accurately predict individual hospital admissions has proved elusive. Here, we incorporate details of the movement of people obtained from mobile phone data into a model that makes accurate predictions of the number of people who will be hospitalized 21 days ahead. This model will be useful for administrators and healthcare workers to plan staffing and discharge of patients to ensure adequate capacity to deal with forthcoming hospital admissions.

Using the Electronic Health Record User Context in Clinical Decision Support Criteria.

BACKGROUND: Computerized clinical decision support (CDS) used in electronic health record systems (EHRs) has led to positive outcomes as well as unintended consequences, such as alert fatigue. Characteristics of the EHR session can be used to restrict CDS tools and increase their relevance, but implications of this approach are not rigorously studied. OBJECTIVES: To assess the utility of using "login location" of EHR users-that is, the location they chose on the login screen-as a variable in the CDS logic. METHODS: We measured concordance between user's login location and the location of the patients they placed orders for and conducted stratified analyses by user groups. We also estimated how often login location data may be stale or inaccurate. RESULTS: One in five CDS alerts incorporated the EHR users' login location into their logic. Analysis of nearly 2 million orders placed by nearly 8,000 users showed that concordance between login location and patient location was high for nurses, nurse practitioners, and physician assistance (all >95%), but lower for fellows (77%) and residents (55%). When providers switched between patients in the EHR, they usually did not update their login location accordingly. CONCLUSION: CDS alerts commonly incorporate user's login location into their logic. User's login location is often the same as the location of the patient the user is providing care for, but substantial discordance can be observed for certain user groups. While this may provide additional information that could be useful to the CDS logic, a substantial amount of discordance happened in specific user groups or when users appeared not to change their login location across different sessions. Those who design CDS alerts should consider a data-driven approach to evaluate the appropriateness of login location for each use case.

Obesity is not associated with adverse outcomes among hospitalized patients with Clostridioides difficile infection.

BACKGROUND: Obesity is associated with increased risk for death in most infections but has not been studied as a risk factor for mortality in Clostridioides difficile infection (CDI). This study tested obesity as a risk factor for death in patients hospitalized with CDI. This was a three-center retrospective study that included hospitalized adults with CDI at Columbia University Irving Medical Center, Brigham and Women's Hospital, and NYU Langone from 2010 to 2018. Multivariate logistic regression was used to assess the relationship between obesity, measured by body mass index, and death from any cause within 30 days after the index CDI test. RESULTS: Data for 3851 patients were analyzed, including 373 (9.7%) who died within 30 days following a diagnosis of CDI. After adjusting for other factors, BMI was not associated with increased risk for death in any BMI category [adjusted OR (aOR) 0.96, 95% CI 0.69 to 1.34 for BMI > 30 vs BMI 20-30; aOR 1.02, 95% CI 0.53 to 1.87 for BMI > 40 vs BMI 20-30]. After stratifying into three groups by age, there were trends towards increased mortality with obesity in the middle-aged (56-75 vs ≤ 55 years old) yet decreased mortality with obesity in the old (> 75 vs ≤ 55) (p = NS for all). Advanced age and low albumin were the factors most strongly associated with death. CONCLUSIONS: We found no association between obesity and death among patients with CDI, in contrast to most other infections. Obesity is not likely to be useful for risk-stratifying hospitalized patients with CDI.