EHR audit logs: A new goldmine for health services research?

A byproduct of the transition to electronic health records (EHRs) is the associated observational data that capture EHR users' granular interactions with the medical record. Often referred to as audit log data or event log data, these datasets capture and timestamp user activity while they are logged in to the EHR. These data - alone and in combination with other datasets - offer a new source of insights, which cannot be gleaned from claims data or clinical data, to support health services research and those studying healthcare processes and outcomes. In this commentary, we seek to promote broader awareness of EHR audit log data and to stimulate their use in many contexts. We do so by describing EHR audit log data and offering a framework for their potential uses in quality domains (as defined by the National Academy of Medicine). The framework is illustrated with select examples in the safety and efficiency domains, along with their accompanying methodologies, which serve as a proof of concept. This article also discusses insights and challenges from working with EHR audit log data. Ensuring that researchers are aware of such data, and the new opportunities they offer, is one way to assure that our healthcare system benefits from the digital revolution.

Assessing the Effectiveness of Engaging Patients and Their Families in the Three-Step Fall Prevention Process Across Modalities of an Evidence-Based Fall Prevention Toolkit: An Implementation Science Study.

BACKGROUND: Patient falls are a major problem in hospitals. The development of a Patient-Centered Fall Prevention Toolkit, Fall TIPS (Tailoring Interventions for Patient Safety), reduced falls by 25% in acute care hospitals by leveraging health information technology to complete the 3-step fall prevention process-(1) conduct fall risk assessments; (2) develop tailored fall prevention plans with the evidence-based interventions; and (3) consistently implement the plan. We learned that Fall TIPS was most effective when patients and family were engaged in all 3 steps of the fall prevention process. Over the past decade, our team developed 3 Fall TIPS modalities-the original electronic health record (EHR) version, a laminated paper version that uses color to provide clinical decision support linking patient-specific risk factors to the interventions, and a bedside display version that automatically populates the bedside monitor with the patients' fall prevention plan based on the clinical documentation in the EHR. However, the relative effectiveness of each Fall TIPS modality for engaging patients and family in the 3-step fall prevention process remains unknown. OBJECTIVE: This study aims to examine if the Fall TIPS modality impacts patient engagement in the 3-step fall prevention process and thus Fall TIPS efficacy. METHODS: To assess patient engagement in the 3-step fall prevention process, we conducted random audits with the question, "Does the patient/family member know their fall prevention plan?" In addition, audits were conducted to measure adherence, defined by the presence of the Fall TIPS poster at the bedside. Champions from 3 hospitals reported data from April to June 2017 on 6 neurology and 7 medical units. Peer-to-peer feedback to reiterate the best practice for patient engagement was central to data collection. RESULTS: Overall, 1209 audits were submitted for the patient engagement measure and 1401 for the presence of the Fall TIPS poster at the bedside. All units reached 80% adherence for both measures. While some units maintained high levels of patient engagement and adherence with the poster protocol, others showed improvement over time, reaching clinically significant adherence (>80%) by the final month of data collection. CONCLUSIONS: Each Fall TIPS modality effectively facilitates patient engagement in the 3-step fall prevention process, suggesting all 3 can be used to integrate evidence-based fall prevention practices into the clinical workflow. The 3 Fall TIPS modalities may prove an effective strategy for the spread, allowing diverse institutions to choose the modality that fits with the organizational culture and health information technology infrastructure.

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.

National Survey of Patient Safety Experiences in Hospital Medicine During the COVID-19 Pandemic.

BACKGROUND: During the COVID-19 pandemic, hospitals were caring for increasing numbers of patients with a novel and highly contagious respiratory illness, forcing adaptations in care delivery. The objective of this study was to understand the impact of these adaptations on patient safety in hospital medicine. METHODS: The authors conducted a nationwide survey to understand patient safety challenges experienced by hospital medicine clinicians during the COVID-19 pandemic. The survey was distributed to members of the Society of Hospital Medicine via an e-mail listserv. It consisted of closed- and open-ended questions to elicit respondents' experience in five domains: error reporting and communication, staffing, equipment, personal protective equipment (PPE) and isolation practices, and infrastructure. Quantitative questions were reported as counts and percentages; qualitative responses were coded and analyzed for relevant themes. RESULTS: Of 196 total responses, 167 respondents (85.2%) were attending physicians and 85 (43.8%) practiced at teaching hospitals. Safety concerns commonly identified included nursing shortages (71.0%), limiting patient interactions to conserve PPE (61.9%), and feeling that one was practicing in a more hazardous environment (61.4%). In free-text responses, clinicians described poor outcomes and patient decompensation due to provider and equipment shortages, as well as communication lapses and diagnostic errors resulting from decreased patient contact and the need to follow isolation protocols. CONCLUSION: Efforts made to accommodate shortages in staff and equipment, adapt to limited PPE, and enforce isolation policies had unintended consequences that affected patient safety and created a more hazardous environment characterized by less efficient care, respiratory decompensations, diagnostic errors, and poor communication with patients.

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.

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.

Examining medication ordering errors using AHRQ network of patient safety databases.

BACKGROUND: Studies examining the effects of computerized order entry (CPOE) on medication ordering errors demonstrate that CPOE does not consistently prevent these errors as intended. We used the Agency for Healthcare Research and Quality (AHRQ) Network of Patient Safety Databases (NPSD) to investigate the frequency and degree of harm of reported events that occurred at the ordering stage, characterized by error type. MATERIALS AND METHODS: This was a retrospective observational study of safety events reported by healthcare systems in participating patient safety organizations from 6/2010 through 12/2020. All medication and other substance ordering errors reported to NPSD via common format v1.2 between 6/2010 through 12/2020 were analyzed. We aggregated and categorized the frequency of reported medication ordering errors by error type, degree of harm, and demographic characteristics. RESULTS: A total of 12 830 errors were reported during the study period. Incorrect dose accounted for 3812 errors (29.7%), followed by incorrect medication 2086 (16.3%), and incorrect duration 765 (6.0%). Of 5282 events that reached the patient and had a known level of severity, 12 resulted in death, 4 resulted in severe harm, 45 resulted in moderate harm, 341 resulted in mild harm, and 4880 resulted in no harm. CONCLUSION: Incorrect dose and incorrect drug orders were the most commonly reported and harmful types of medication ordering errors. Future studies should aim to develop and test interventions focused on CPOE to prevent medication ordering errors, prioritizing wrong-dose and wrong-drug errors.

Cost of Inpatient Falls and Cost-Benefit Analysis of Implementation of an Evidence-Based Fall Prevention Program.

Importance: There is insufficient research on the costs of patient falls in health care systems, a leading source of nonreimbursable adverse events. Objective: To report the costs of inpatient falls and the cost savings associated with implementation of an evidence-based fall prevention program. Design, Setting, and Participants: In this economic evaluation, a matched case-control study used the findings from an interrupted time series analysis that assessed changes in fall rates following implementation of an evidence-based fall prevention program to understand the cost of inpatient falls. An economic analysis was then performed to assess the cost benefits associated with program implementation across 2 US health care systems from June 1, 2013, to August 31, 2019, in New York, New York, and Boston, Massachusetts. All adults hospitalized in participating units were included in the analysis. Data analysis was performed from October 2021 to November 2022. Interventions: Evidence-based fall prevention program implemented in 33 medical and surgical units in 8 hospitals. Main Outcomes and Measures: Primary outcome was cost of inpatient falls. Secondary outcome was the costs and cost savings associated with the evidence-based fall prevention program. Results: A total of 10 176 patients who had a fall event (injurious or noninjurious) with 29 161 matched controls (no fall event) were included in the case-control study and the economic analysis (51.9% were 65-74 years of age, 67.1% were White, and 53.6% were male). Before the intervention, there were 2503 falls and 900 injuries; after the intervention, there were 2078 falls and 758 injuries. Based on a 19% reduction in falls and 20% reduction in injurious falls from the beginning to the end of the postintervention period, the economic analysis demonstrated that noninjurious and injurious falls were associated with cost increases of $35 365 and $36 776, respectively. The implementation of the evidence-based fall prevention program was associated with $14 600 in net avoided costs per 1000 patient-days. Conclusions and Relevance: This economic evaluation found that fall-related adverse events represented a clinical and financial burden to health care systems and that the current Medicare policy limits reimbursement. In this study, costs of falls only differed marginally by injury level. Policies that incentivize organizations to implement evidence-based strategies that reduce the incidence of all falls may be effective in reducing both harm and costs.

"What's in a name?" Identification of newborn infants at birth using their given names.

OBJECTIVE: To determine the proportion of pregnant women who selected names for their babies to be born and were willing to disclose them for use in hospital systems, thereby potentially reducing infant identification errors. STUDY DESIGN: Survey of pregnant women admitted to postpartum or antepartum units at a large academic hospital. Descriptive analyses were conducted to determine the proportion who had chosen names prior to delivery. Chi-square tests and calculated odds ratios assessed the association with demographic and pregnancy factors. RESULTS: Of postpartum participants, 79.0% had names for their newborns at birth. This proportion was significantly lower in self-identified non-Hispanic, white, and married women. Of antepartum participants, 65.7% had selected a name at the time of survey. CONCLUSION: Most participants had names chosen for use at birth. This finding was consistent across demographic and pregnancy characteristics, supporting the feasibility of using given names for newborns in hospital systems at birth.

Critical Care Clinicians' Experiences of Patient Safety During the COVID-19 Pandemic.

OBJECTIVE: It is unknown how hospital- and systems-level factors have impacted patient safety in the intensive care unit (ICU) during the COVID-19 pandemic. We sought to understand how the pandemic has exacerbated preexisting patient safety issues and created novel patient safety challenges in ICUs in the United States. METHODS: We performed a national, multi-institutional, mixed-methods survey of critical care clinicians to elicit experiences related to patient safety during the pandemic. The survey was disseminated via email through the Society of Critical Care Medicine listserv. Data were reported as valid percentages, compared by COVID caseload and peak of the pandemic; free-text responses were analyzed and coded for themes. RESULTS: We received 335 survey responses. On general patient safety, 61% felt that conditions were more hazardous when compared with the prepandemic period. Those who took care of mostly COVID-19 patients were more likely to perceive that care was more hazardous (odds ratio, 4.89; 95% CI, 2.49-9.59) compared with those who took care of mostly non-COVID-19 or no COVID-19 patients. In free-text responses, providers identified patient safety risks related to pandemic adaptations, such as ventilator-related lung injury, medication and diagnostic errors, oversedation, oxygen device removal, and falls. CONCLUSIONS: Increased COVID-19 case burden was significantly associated with perceptions of a less safe patient care environment by frontline ICU clinicians. Results of the qualitative analysis identified specific patient safety hazards in ICUs across the United States as downstream consequences of hospital and provider strain during periods of the COVID-19 pandemic.

Indication alerts to improve problem list documentation.

BACKGROUND: Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication. METHODS: We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review. RESULTS: Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P<.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%. CONCLUSIONS: Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.

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.

Development and Validation of a Fall Prevention Efficiency Scale.

OBJECTIVES: Fall TIPS (Tailoring Interventions for Patient Safety) is an evidence-based fall prevention program that led to a 25% reduction in falls in hospitalized adults. Because it would be helpful to assess nurses' perceptions of burdens imposed on them by using Fall TIPS or other fall prevention program, we conducted a study to learn benefits and burdens. METHODS: A 3-phase mixed-method study was conducted at 3 hospitals in Massachusetts and 3 in New York: (1) initial qualitative, elicited and categorized nurses' views of time spent implementing Fall TIPS; (2) second qualitative, used nurses' quotes to develop items, research team inputs for refinement and organization, and clinical nurses' evaluation and suggestions to develop the prototype scale; and (3) quantitative, evaluated psychometric properties. RESULTS: Four "time" themes emerged: (1) efficiency, (2) inefficiency, (3) balances out, and (4) valued. A 20-item prototype Fall Prevention Efficiency Scale was developed, administered to 383 clinical nurses, and reduced to 13 items. Individual items demonstrated robust stability with Pearson correlations of 0.349 to 0.550 and paired t tests of 0.155 to 1.636. Four factors explained 74.3% variance and provided empirical support for the scale's conceptual basis. The scale achieved excellent internal consistency values (0.82-0.92) when examined with the test, validation, and paired (both test and retest) samples. CONCLUSIONS: This new scale assess nurses' perceptions of how a fall prevention program affects their efficiency, which impacts the likelihood of use. Learning nurses' beliefs about time wasted when implementing new programs allows hospitals to correct problems that squander time.

Wrong-Patient Orders in Obstetrics.

OBJECTIVE: To compare rates of wrong-patient orders among patients on obstetric units compared with reproductive-aged women admitted to medical-surgical units. METHODS: This was an observational study conducted in a large health system in New York between January 1, 2016, and December 31, 2018. The primary outcome was near-miss wrong-patient orders identified using the National Quality Forum-endorsed Wrong-Patient Retract-and-Reorder measure. All electronic orders placed for eligible patients during the study period were extracted retrospectively from the health system data warehouse, and the unit of analysis was the order session (consecutive orders placed by a single clinician for a patient within 60 minutes). Multilevel logistic regression models were used to estimate odds ratios (ORs) and 95% CIs comparing the probability of retract-and-reorder events in obstetric and medical-surgical units, overall, and in subgroups defined by clinician type and order timing. RESULTS: Overall, 1,329,463 order sessions were placed during the study period, including 676,643 obstetric order sessions (from 45,436 patients) and 652,820 medical-surgical order sessions (from 12,915 patients). The rate of 79.5 retract-and-reorder events per 100,000 order sessions in obstetric units was significantly higher than the rate in the general medical-surgical population of 42.3 per 100,000 order sessions (OR 1.98, 95% CI 1.64-2.39). The obstetric retract-and-reorder event rate was significantly higher for attending physicians and house staff compared with advanced practice clinicians. There were no significant differences in error rates between day and night shifts. CONCLUSION: Order errors occurred more frequently on obstetric units compared with medical-surgical units. Systems strategies shown to decrease these events in other high-risk specialties should be explored in obstetrics to render safer maternity care.

Validating Fall Prevention Icons to Support Patient-Centered Education.

INTRODUCTION: Falls with injury are the most prevalent hospital adverse event. The objective of this project was to refine fall risk and prevention icons for a patient-centric bedside toolkit to promote patient and nurse engagement in accurately assessing fall risks and developing a tailored fall prevention plan. METHODS: Eighty-eight patients and 60 nurses from 2 academic medical centers participated in 4 iterations of testing to refine 6 fall risk and 10 fall prevention icons. During individual interviews, participants rated their satisfaction with the degree to which that icon represented the concept on a 4-point Likert scale, enabling computation of a Content Validity Index (CVI), and provided comments and suggestions for improvement. After reviewing CVI scores and feedback, the research team consulted with the illustrator to revise the icons. RESULTS: Content Validity Index scores improved after icon modifications. Icons that depicted multiple concepts required further iterations to be acceptable. DISCUSSION: Using icons to depict an accurate and easy-to-interpret fall risk assessment and intervention plan for all care team members including patients and family to follow should lead to improved adherence with that plan and decreased falls. CONCLUSIONS: All 16 icons were refined and used to form the basis for a bedside fall prevention toolkit.

Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. MATERIALS AND METHODS: For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output. RESULTS: The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. DISCUSSION: Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. CONCLUSIONS: We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.