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.

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.

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.

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.

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.

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.).

Association Between Electronic Health Record Time and Quality of Care Metrics in Primary Care.

Importance: Physicians across the US spend substantial time working in the electronic health record (EHR), with primary care physicians (PCPs) spending the most time. The association between EHR time and ambulatory care quality outcomes is unclear. Objective: To characterize measures of EHR use and ambulatory care quality performance among PCPs. Design, Setting, and Participants: A cross-sectional study of PCPs with longitudinal patient panels using a single EHR vendor was conducted at Brigham and Women's Hospital and Massachusetts General Hospital during calendar year 2021. Exposures: Independent variables included PCPs demographic and practice characteristics and EHR time measures (PCP-level mean of daily total EHR time, after-hours time, time from 5:30 pm to 7:00 am and time on weekends, and daily EHR time on notes, sending and receiving patient, staff, results, prescription, or system messages [in-basket], and clinical review). Main Outcomes and Measures: Outcome variables were ambulatory quality measures (year-end, PCP panel-level achievement of targets for hemoglobin A1c level control, lipid management, hypertension control, diabetes screening, and breast cancer screening). Results: The sample included 291 physicians (174 [59.8%] women). Median panel size was 829 (IQR, 476-1157) patients and mean (SD) clinical full-time equivalent was 0.54 (0.27). The PCPs spent a mean (SD) of 145.9 (64.6) daily minutes on the EHR. There were significant associations between EHR time and panel-level achievement of hemoglobin A1c control, hypertension control, and breast cancer screening targets. In adjusted analyses, each additional 15 minutes of total daily EHR time was associated with 0.58 (95% CI, 0.32-0.84) percentage point greater panel-level hemoglobin A1c control, 0.52 (95% CI, 0.33-0.71) percentage point greater hypertension control, and 0.28 (95% CI, 0.05-0.52) higher breast cancer screening rates. Each daily additional 15 minutes of in-basket time was associated with 2.26 (95% CI, 1.05-3.48) greater panel-wide hemoglobin A1c control, 1.65 (95% CI, 0.83-2.47) percentage point greater hypertension control, and 1.26 (95% CI, 0.51-2.02) percentage point higher breast cancer screening rates. Associations were largely concentrated among PCPs with 0.5 clinical full-time equivalent or less. There were no associations between EHR use metrics and diabetes screening or lipid management in patients with cardiovascular disease. Conclusions and Relevance: This cross-sectional study found an association between EHR time and some measures of ambulatory care quality. Although increased EHR time is associated with burnout, it may represent a level of thoroughness or communication that enhances certain outcomes. It may be useful for future studies to characterize payment models, workflows, and technologies that enable high-quality ambulatory care delivery while minimizing EHR burden.

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.

Observation and Patients' Perceptions of Incorporating Their Photograph Into the Electronic Health Record.

OBJECTIVES: Wrong-patient errors are common and have the potential to cause serious harm. The Office of the National Coordinator for Health Information Technology Patient Identification SAFER Guide recommends displaying patient photographs in electronic health record (EHR) systems to facilitate patient identification and reduce wrong-patient errors. A potential barrier to implementation is patient refusal; however, patients' perceptions about having their photograph captured during registration and integrated into the EHR are unknown. METHODS: The study was conducted in an emergency department (ED) and primary care outpatient clinic within a large integrated health system in New York City. The study consisted of 2 components: (1) direct observation of the registration process to quantify the frequency of patient refusals and (2) semistructured interviews to elicit patients' feedback on perceived benefits and barriers to integrating their photograph into the EHR. RESULTS: Of 172 registrations where patients were asked to take a photograph for patient identification, 0 refusals were observed (ED, 0 of 87; primary care outpatient clinic, 0 of 85). A convenience sample of 30 patients were interviewed (female, 70%; age ≥55 years, 43%; Hispanic/Latino, 67%; Black, 23%). Perceived benefits of integrating patient photographs into the EHR included improved security (40%), improved patient identification (23%), and ease of registration (17%). A small proportion of patients raised privacy concerns. CONCLUSIONS: Patient refusal was not found to be a barrier to implementation of patient photographs in the EHR. Efforts to identify and address other potential barriers would help ensure that the highest proportion of patients has photographs in their medical record.

A Prediction Model Incorporating Peripheral Eosinopenia as a Novel Risk Factor for Death After Hospitalization for Clostridioides difficile Infection.

BACKGROUND AND AIMS: Clostridioides difficile infection (CDI) is associated with a range of outcomes, and existing prediction models for death among patients with CDI are imprecise. Peripheral eosinopenia has been proposed as a novel risk factor for death among patients with CDI but has not been incorporated into prediction models. This study aimed to develop and validate a prediction model for death among patients hospitalized with CDI that incorporated peripheral eosinopenia. METHODS: Eosinopenia was defined as 0 eosinophils/µL on the soonest peripheral blood drawn within the 48-hour window of the CDI test (before or after). Adults were eligible for the study if they were hospitalized at any one of 3 large, unaffiliated hospital networks, tested positive for CDI by stool polymerase chain reaction, and received appropriate anti-CDI treatment. Patients were followed for all-cause death for up to 30 days. RESULTS: There were 4518 unique hospitalized adults with CDI included (2142 in the derivation cohort and 2376 in the validation cohort). All-cause 30-day mortality was 9% and 10% in the cohorts. In the validation cohort, the factors most strongly associated with death were eosinopenia (adjusted odds ratio [aOR] 2.49, 95% confidence interval [CI] 1.77-3.50), albumin <3 g/dL (aOR 3.26, 95% CI 2.13-3.49), and creatinine >1.5 mg/dL (aOR 2.55, 95% CI 1.86-3.49). A 6-variable clinical prediction model was developed that improved on existing classification schemes for CDI severity (area under the receiver operating characteristic curve of 0.75 vs 0.68). CONCLUSION: Among adults hospitalized with CDI, peripheral eosinopenia was associated with increased risk of all-cause 30-day mortality. A prediction model incorporating peripheral eosinopenia was developed to improve care for hospitalized patients with CDI through risk stratification.

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.

Coronavirus disease 2019 (COVID-19) screening system utilizing daily symptom attestation helps identify hospital employees who should be tested to protect patients and coworkers.

OBJECTIVE: To investigate the effectiveness of a daily attestation system used by employees of a multi-institutional academic medical center, which comprised of symptom-screening, self-referrals to the Occupational Health Services team, and/or a severe acute respiratory coronavirus virus 2 (SARS-CoV-2) test. DESIGN: We conducted a retrospective cohort study of all employee attestations and SARS-CoV-2 tests performed between March and June 2020. SETTING: A large multi-institutional academic medical center, including both inpatient and ambulatory settings. PARTICIPANTS: All employees who worked at the study site. METHODS: Data were combined from the attestation system (COVIDPass), the employee database, and the electronic health records and were analyzed using descriptive statistics including χ2, Wilcoxon, and Kruskal-Wallis tests. We investigated whether an association existed between symptomatic attestations by the employees and the employee testing positive for SARS-CoV-2. RESULTS: After data linkage and cleaning, there were 2,117,298 attestations submitted by 65,422 employees between March and June 2020. Most attestations were asymptomatic (99.9%). The most commonly reported symptoms were sore throat (n = 910), runny nose (n = 637), and cough (n = 570). Among the 2,026 employees who ever attested that they were symptomatic, 905 employees were tested within 14 days of a symptomatic attestation, and 114 (13%) of these tests were positive. The most common symptoms associated with a positive SARS-CoV-2 test were anosmia (23% vs 4%) and fever (46% vs 19%). CONCLUSIONS: Daily symptom attestations among healthcare workers identified a handful of employees with COVID-19. Although the number of positive tests was low, attestations may help keep unwell employees off campus to prevent transmissions.

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.

Low Efficacy of Medication Shortage Clinical Decision Support Alerts.

OBJECTIVE: We examined clinical decision support (CDS) alerts designed specifically for medication shortages to characterize and assess provider behavior in response to these short-term clinical situations. MATERIALS AND METHODS: We conducted a retrospective analysis of the usage of medication shortage alerts (MSAs) that included at least one alternative medication suggestion and were active for 60 or more days during the 2-year study period, January 1, 2018 to December 31, 2019, in a large health care system. We characterized ordering provider behavior in response to inpatient MSAs. We then developed a linear regression model to predict provider response to alerts using the characteristics of the ordering provider and alert frequency groupings. RESULTS: During the study period, there were 67 MSAs in use that focused on 42 distinct medications in shortage. The MSAs suggested an average of 3.9 alternative medications. Adjusting for the different alerts, fellows (p = 0.004), residents (p = 0.03), and physician assistants (p = 0.02) were less likely to accept alerts on average compared with attending physicians. Further, female ordering clinicians (p < 0.001) were more likely to accept alerts on average compared with male ordering clinicians. CONCLUSION: Our findings demonstrate that providers tended to reject MSAs, even those who were sometimes flexible about their responses. The low overall acceptance rate supports the theory that alerts appearing at the time of order entry may have limited value, as they may be presented too late in the decision-making process. Though MSAs are designed to be attention-grabbing and higher impact than traditional CDS, our findings suggest that providers rarely change their clinical decisions when presented with these alerts.

An analysis of the structure and content of dashboards used to monitor patient safety in the inpatient setting.

The objective of this study is to review and compare patient safety dashboards used by hospitals and identify similarities and differences in their design, format, and scope. We reviewed design features of electronic copies of patient safety dashboards from a representative sample of 10 hospitals. The results show great heterogeneity in the format, presentation, and scope of patient safety dashboards. Hospitals varied in their use of performance indicators (targets, trends, and benchmarks), style of color coding, and timeframe for the displayed metrics. The average number of metrics per dashboard display was 28, with a wide range from 7 to 84. Given the large variation in dashboard design, there is a need for future work to assess which approaches are associated with the best outcomes, and how specific elements contribute to usability, to help customize dashboards to meet the needs of different clinical, and operational stakeholders.

Published models that predict hospital readmission: a critical appraisal.

INTRODUCTION: The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness. OBJECTIVE: To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation. METHODS: We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. Guided by CAMPR, two researchers independently appraised published readmission models in two recent systematic reviews and concurrently extracted data to generate reference lists of eligibility criteria and risk factors. RESULTS: We found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%). CONCLUSIONS: The high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.