Physician Time Spent Using the Electronic Health Record During Outpatient Encounters: A Descriptive Study.

Background: The amount of time that providers spend using electronic health records (EHRs) to support the care delivery process is a concern for the U.S. health care system. Given the potential effect on patient care and the high costs related to this time, particularly for medical specialists whose work is largely cognitive, these findings warrant more precise documentation of the time physicians invest in these clinically focused EHR functions. Objective: To describe how much time ambulatory medical subspecialists and primary care physicians across several U.S. care delivery systems spend on various EHR functions. Design: Descriptive study. Setting: U.S.-based, adult, nonsurgical, ambulatory practices using the Cerner Millennium EHR. Participants: 155 000 U.S. physicians. Measurements: Data were extracted from software log files in the Lights On Network (Cerner) during 2018 that totaled the time spent on each of the 13 clinically focused EHR functions. Averages per encounter by specialty were computed. Results: This study included data from approximately 100 million patient encounters with about 155 000 physicians from 417 health systems. Physicians spent an average of 16 minutes and 14 seconds per encounter using EHRs, with chart review (33%), documentation (24%), and ordering (17%) functions accounting for most of the time. The distribution of time spent by providers using EHRs varies greatly within specialty. The proportion of time spent on various clinically focused functions was similar across specialties. Limitation: Variation by health system could not be examined, and all providers used the same software. Conclusion: The time spent using EHRs to support care delivery constitutes a large portion of the physicians' day, and wide variation suggests opportunities to optimize systems and processes. Primary Funding Source: None.

Technology-Enabled Clinical Trials: Transforming Medical Evidence Generation.

The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.

Explaining Variability in Electronic Health Record Effort in Primary Care Ambulatory Encounters.

BACKGROUND: Electronic health record (EHR) user interface event logs are fast providing another perspective on the value and efficiency EHR technology brings to health care. Analysis of these detailed usage data has demonstrated their potential to identify EHR and clinical process design factors related to user efficiency, satisfaction, and burnout. OBJECTIVE: This study aimed to analyze the event log data across 26 different health systems to determine the variability of use of a single vendor's EHR based on four event log metrics, at the individual, practice group, and health system levels. METHODS: We obtained de-identified event log data recorded from June 1, 2018, to May 31, 2019, from 26 health systems' primary care physicians. We estimated the variability in total Active EHR Time, Documentation Time, Chart Review Time, and Ordering Time across health systems, practice groups, and individual physicians. RESULTS: In total, 5,444 physicians (Family Medicine: 3,042 and Internal Medicine: 2,422) provided care in a total of 2,285 different practices nested in 26 health systems. Health systems explain 1.29, 3.55, 3.45, and 3.30% of the total variability in Active Time, Documentation Time, Chart Review Time, and Ordering Time, respectively. Practice-level variability was estimated to be 7.96, 13.52, 8.39, and 5.57%, respectively, and individual physicians explained the largest proportion of the variability for those same outcomes 17.09, 27.49, 17.51, and 19.75%, respectively. CONCLUSION: The most variable physician EHR usage patterns occurs at the individual physician level and decreases as you move up to the practice and health system levels. This suggests that interventions to improve individual users' EHR usage efficiency may have the most potential impact compared with those directed at health system or practice levels.

A regional informatics platform for coordinated antibiotic-resistant infection tracking, alerting, and prevention.

BACKGROUND: We developed and assessed the impact of a patient registry and electronic admission notification system relating to regional antimicrobial resistance (AMR) on regional AMR infection rates over time. We conducted an observational cohort study of all patients identified as infected or colonized with methicillin-resistant Staphylococcus aureus (MRSA) and/or vancomycin-resistant enterococci (VRE) on at least 1 occasion by any of 5 healthcare systems between 2003 and 2010. The 5 healthcare systems included 17 hospitals and associated clinics in the Indianapolis, Indiana, region. METHODS: We developed and standardized a registry of MRSA and VRE patients and created Web forms that infection preventionists (IPs) used to maintain the lists. We sent e-mail alerts to IPs whenever a patient previously infected or colonized with MRSA or VRE registered for admission to a study hospital from June 2007 through June 2010. RESULTS: Over a 3-year period, we delivered 12 748 e-mail alerts on 6270 unique patients to 24 IPs covering 17 hospitals. One in 5 (22%-23%) of all admission alerts was based on data from a healthcare system that was different from the admitting hospital; a few hospitals accounted for most of this crossover among facilities and systems. CONCLUSIONS: Regional patient registries identify an important patient cohort with relevant prior antibiotic-resistant infection data from different healthcare institutions. Regional registries can identify trends and interinstitutional movement not otherwise apparent from single institution data. Importantly, electronic alerts can notify of the need to isolate early and to institute other measures to prevent transmission.

Evaluating the impact of database heterogeneity on observational study results.

Clinical studies that use observational databases to evaluate the effects of medical products have become commonplace. Such studies begin by selecting a particular database, a decision that published papers invariably report but do not discuss. Studies of the same issue in different databases, however, can and do generate different results, sometimes with strikingly different clinical implications. In this paper, we systematically study heterogeneity among databases, holding other study methods constant, by exploring relative risk estimates for 53 drug-outcome pairs and 2 widely used study designs (cohort studies and self-controlled case series) across 10 observational databases. When holding the study design constant, our analysis shows that estimated relative risks range from a statistically significant decreased risk to a statistically significant increased risk in 11 of 53 (21%) of drug-outcome pairs that use a cohort design and 19 of 53 (36%) of drug-outcome pairs that use a self-controlled case series design. This exceeds the proportion of pairs that were consistent across databases in both direction and statistical significance, which was 9 of 53 (17%) for cohort studies and 5 of 53 (9%) for self-controlled case series. Our findings show that clinical studies that use observational databases can be sensitive to the choice of database. More attention is needed to consider how the choice of data source may be affecting results.

The role of healthcare IT: becoming a learning organization.

IT advances that will support healthcare providers' transition toward becoming "learning organizations" include the following: The increase in big data" (patient data captured in EHRs, coupled with data from imaging, molecular medicine, patient-provided data, and insurance claims). Real-time analytics and novel decision aids. Ease-of-use advancements and effective data capture methods. Efforts to increase facile interoperability. Extended reach of EHRs in gathering data from other processes and sources

One-Year Adverse Outcomes Among US Adults With Post-COVID-19 Condition vs Those Without COVID-19 in a Large Commercial Insurance Database.

Importance: Many individuals experience ongoing symptoms following the onset of COVID-19, characterized as postacute sequelae of SARS-CoV-2 or post-COVID-19 condition (PCC). Less is known about the long-term outcomes for these individuals. Objective: To quantify 1-year outcomes among individuals meeting a PCC definition compared with a control group of individuals without COVID-19. Design, Setting, and Participants: This case-control study with a propensity score-matched control group included members of commercial health plans and used national insurance claims data enhanced with laboratory results and mortality data from the Social Security Administration's Death Master File and Datavant Flatiron data. The study sample consisted of adults meeting a claims-based definition for PCC with a 2:1 matched control cohort of individuals with no evidence of COVID-19 during the time period of April 1, 2020, to July 31, 2021. Exposures: Individuals experiencing postacute sequelae of SARS-CoV-2 using a Centers for Disease Control and Prevention-based definition. Main Outcomes and Measures: Adverse outcomes, including cardiovascular and respiratory outcomes and mortality, for individuals with PCC and controls assessed over a 12-month period. Results: The study population included 13 435 individuals with PCC and 26 870 individuals with no evidence of COVID-19 (mean [SD] age, 51 [15.1] years; 58.4% female). During follow-up, the PCC cohort experienced increased health care utilization for a wide range of adverse outcomes: cardiac arrhythmias (relative risk [RR], 2.35; 95% CI, 2.26-2.45), pulmonary embolism (RR, 3.64; 95% CI, 3.23-3.92), ischemic stroke (RR, 2.17; 95% CI, 1.98-2.52), coronary artery disease (RR, 1.78; 95% CI, 1.70-1.88), heart failure (RR, 1.97; 95% CI, 1.84-2.10), chronic obstructive pulmonary disease (RR, 1.94; 95% CI, 1.88-2.00), and asthma (RR, 1.95; 95% CI, 1.86-2.03). The PCC cohort also experienced increased mortality, as 2.8% of individuals with PCC vs 1.2% of controls died, implying an excess death rate of 16.4 per 1000 individuals. Conclusions and Relevance: This case-control study leveraged a large commercial insurance database and found increased rates of adverse outcomes over a 1-year period for a PCC cohort surviving the acute phase of illness. The results indicate a need for continued monitoring for at-risk individuals, particularly in the area of cardiovascular and pulmonary management.

Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership.

BACKGROUND: Expanded availability of observational healthcare data (both administrative claims and electronic health records) has prompted the development of statistical methods for identifying adverse events associated with medical products, but the operating characteristics of these methods when applied to the real-world data are unknown. METHODS: We studied the performance of eight analytic methods for estimating of the strength of association-relative risk (RR) and associated standard error of 53 drug-adverse event outcome pairs, both positive and negative controls. The methods were applied to a network of ten observational healthcare databases, comprising over 130 million lives. Performance measures included sensitivity, specificity, and positive predictive value of methods at RR thresholds achieving statistical significance of p < 0.05 or p < 0.001 and with absolute threshold RR > 1.5, as well as threshold-free measures such as area under receiver operating characteristic curve (AUC). RESULTS: Although no specific method demonstrated superior performance, the aggregate results provide a benchmark and baseline expectation for risk identification method performance. At traditional levels of statistical significance (RR > 1, p < 0.05), all methods have a false positive rate >18%, with positive predictive value <38%. The best predictive model, high-dimensional propensity score, achieved an AUC = 0.77. At 50% sensitivity, false positive rate ranged from 16% to 30%. At 10% false positive rate, sensitivity of the methods ranged from 9% to 33%. CONCLUSIONS: Systematic processes for risk identification can provide useful information to supplement an overall safety assessment, but assessment of methods performance suggests a substantial chance of identifying false positive associations.

Data for drugs available through low-cost prescription drug programs are available through pharmacy benefit manager and claims data.

BACKGROUND: Observational data are increasingly being used for pharmacoepidemiological, health services and clinical effectiveness research. Since pharmacies first introduced low-cost prescription programs (LCPP), researchers have worried that data about the medications provided through these programs might not be available in observational data derived from administrative sources, such as payer claims or pharmacy benefit management (PBM) company transactions. METHOD: We used data from the Indiana Network for Patient Care to estimate the proportion of patients with type 2 diabetes to whom an oral hypoglycemic agent was dispensed. Based on these estimates, we compared the proportions of patients who received medications from chains that do and do not offer an LCPP, the proportion trend over time based on claims data from a single payer, and to proportions estimated from the Medical Expenditure Panel Survey (MEPS). RESULTS: We found that the proportion of patients with type 2 diabetes who received oral hypoglycemic medications did not vary based on whether the chain that dispensed the drug offered an LCPP or over time. Additionally, the rates were comparable to those estimated from MEPS. CONCLUSION: Researchers can be reassured that data for medications available through LCPPs continue to be available through administrative data sources.

Assessment of structured data elements for social risk factors.

OBJECTIVES: Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. STUDY DESIGN: Technical expert panel. METHODS: A 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias). RESULTS: Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors. CONCLUSIONS: Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.

Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

The U.S. Food and Drug Administration (FDA) Amendments Act of 2007 mandated that the FDA develop a system for using automated health care data to identify risks of marketed drugs and other medical products. The Observational Medical Outcomes Partnership is a public-private partnership among the FDA, academia, data owners, and the pharmaceutical industry that is responding to the need to advance the science of active medical product safety surveillance by using existing observational databases. The Observational Medical Outcomes Partnership's transparent, open innovation approach is designed to systematically and empirically study critical governance, data resource, and methodological issues and their interrelationships in establishing a viable national program of active drug safety surveillance by using observational data. This article describes the governance structure, data-access model, methods-testing approach, and technology development of this effort, as well as the work that has been initiated.

Pediatrician Electronic Health Record Time Use for Outpatient Encounters.

BACKGROUND: The time providers spend using their electronic health records (EHRs) delivering care and its potential impact on patient care are of concern for the health care system. In studies to date, researchers have focused on providers who primarily care for adults. Scant information exists for pediatricians. Given this gap, it is important to quantify EHR activity for this group. METHODS: We studied pediatricians practicing in US-based ambulatory practices using the Cerner Millennium EHR by extracting data from software log files in the Lights On Network for the calendar year 2018 and summarizing the time spent on each of 13 clinically-focused EHR functions according to clinical specialty. RESULTS: Our data included >20 million encounters by almost 30 thousand physicians from 417 health systems. Pediatric physicians spent an average of 16 minutes per encounter using their EHR. Chart review (31%), documentation (31%), and ordering (13%) functions accounted for most of the time. The distribution of time spent by providers using their EHR is highly variable within subspecialty but is similar across specialties. Because of data limitations, we were unable to examine geographic or health system-specific variation. CONCLUSIONS: Pediatricians, like physicians who care for adults, spend a large portion of their day using their EHR. Additionally, although chart review and documentation accounted for 62% of the activity, as in previously published studies, in our study, we found that chart review accounted for half of that time. Wide variation suggests opportunities to optimize both the processes of entering information and searching for patient data within the EHR.

Prior presumed coronavirus infection reduces COVID-19 risk: A cohort study.

BACKGROUND: Immunological cross-reactivity between common cold coronaviruses (CCC) and SARS-CoV-2 might account for the reduced incidence of COVID-19 in children. Evidence to support speculation includes in vitro evidence for humoral and cellular cross-reactivity with SARS-CoV-2 in specimens obtained before the pandemic started. METHOD: We used retrospective health insurance enrollment records, claims, and laboratory results to assemble a cohort of 869,236 insured individuals who had a PCR test for SARS-CoV-2. We estimated the effects of having clinical encounters for various diagnostic categories in the year preceding the study period on the risk of a positive test result. FINDINGS: After adjusting for age, gender and care seeking behavior, we identified that individuals with diagnoses for common cold symptoms, including acute sinusitis, bronchitis, or pharyngitis in the preceding year had a lower risk of testing positive for SARS-CoV-2 (OR=0.76, 95%CI=0.75, 0.77). No reduction in the odds of a positive test for SARS-CoV-2 was seen in individuals under 18 years. The reduction in odds in adults remained stable for four years but was strongest in those with recent common cold symptoms. INTERPRETATION: While this study cannot attribute this association to cross-immunity resulting from a prior CCC infection, it is one potential explanation. Regardless of the cause, the reduction in the odds of being infected by SARS-CoV-2 among those with a recent diagnosis of common cold symptoms may have a role in shifting future COVD-19 infection patterns from endemic to episodic.

The development of a highly constrained health level 7 implementation guide to facilitate electronic laboratory reporting to ambulatory electronic health record systems.

Electronic laboratory interfaces can significantly increase the value of ambulatory electronic health record (EHR) systems by providing laboratory result data automatically and in a computable form. However, many ambulatory EHRs cannot implement electronic laboratory interfaces despite the existence of messaging standards, such as Health Level 7, version 2 (HL7). Among several barriers to implementing laboratory interfaces is the extensive optionality within the HL7 message standard. This paper describes the rationale for and development of an HL7 implementation guide that seeks to eliminate most of the optionality inherent in HL7, but retain the information content required for reporting outpatient laboratory results. A work group of heterogeneous stakeholders developed the implementation guide based on a set of design principles that emphasized parsimony, practical requirements, and near-term adoption. The resulting implementation guide contains 93% fewer optional data elements than HL7. This guide was successfully implemented by 15 organizations during an initial testing phase and has been approved by the HL7 standards body as an implementation guide for outpatient laboratory reporting. Further testing is required to determine whether widespread adoption of the implementation guide by laboratories and EHR systems can facilitate the implementation of electronic laboratory interfaces.

Core content for the subspecialty of clinical informatics.

The Core Content for Clinical Informatics defines the boundaries of the discipline and informs the Program Requirements for Fellowship Education in Clinical Informatics. The Core Content includes four major categories: fundamentals, clinical decision making and care process improvement, health information systems, and leadership and management of change. The AMIA Board of Directors approved the Core Content for Clinical Informatics in November 2008.

Assessing HIE stakeholder readiness for consumer access: lessons learned from the NHIN trial implementations.

Personal health records (PHRs) have the potential to empower patient decision-making. Integrating PHRs into the nation's health information infrastructure via the Nationwide Health Information Network (NHIN) may accelerate their adoption and use. PHR and NHIN technical development activities are advancing, but little is known about provider acceptance of PHR usage in this manner. Researchers conducted semi-structured interviews with organizations participating in an operational health information exchange to elicit opinions regarding such integration. The conversations identified important concerns that need to be addressed in order to achieve the vision established in the Consumer Access to Clinical Information Use Case outlined by the American Health Information Community. These challenges include provider workflow, authentication of consumer access, impact on provider-patient communication and consumer health literacy. Developers, policymakers, providers and patients should work together to confront and find solutions to these challenges to achieve the full potential of PHRs in the healthcare system.

Sepsis surveillance: an examination of parameter sensitivity and alert reliability.

OBJECTIVE: To examine performance of a sepsis surveillance system in a simulated environment where modifications to parameters and settings for identification of at-risk patients can be explored in-depth. MATERIALS AND METHODS: This was a multiple center observational cohort study. The study population comprised 14 917 adults hospitalized in 2016. An expert-driven rules algorithm was applied against 15.1 million data points to simulate a system with binary notification of sepsis events. Three system scenarios were examined: a scenario as derived from the second version of the Consensus Definitions for Sepsis and Septic Shock (SEP-2), the same scenario but without systolic blood pressure (SBP) decrease criteria (near SEP-2), and a conservative scenario with limited parameters. Patients identified by scenarios as being at-risk for sepsis were assessed for suspected infection. Multivariate binary logistic regression models estimated mortality risk among patients with suspected infection. RESULTS: First, the SEP-2-based scenario had a hyperactive, unreliable parameter SBP decrease >40 mm Hg from baseline. Second, the near SEP-2 scenario demonstrated adequate reliability and sensitivity. Third, the conservative scenario had modestly higher reliability, but sensitivity degraded quickly. Parameters differed in predicting mortality risk and represented a substitution effect between scenarios. DISCUSSION: Configuration of parameters and alert criteria have implications for patient identification and predicted outcomes. CONCLUSION: Performance of scenarios was associated with scenario design. A single hyperactive, unreliable parameter may negatively influence adoption of the system. A trade-off between modest improvements in alert reliability corresponded to a steep decline in condition sensitivity in scenarios explored.

A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions.

OBJECTIVES: We examined whether automated electronic laboratory reporting of notifiable-diseases results in information being delivered to public health departments more completely and quickly than is the case with spontaneous, paper-based reporting. METHODS: We used data from a local public health department, hospital infection control departments, and a community-wide health information exchange to identify all potential cases of notifiable conditions that occurred in Marion County, Ind, during the first quarter of 2001. We compared traditional spontaneous reporting to the health department with automated electronic laboratory reporting through the health information exchange. RESULTS: After reports obtained using the 2 methods had been matched, there were 4785 unique reports for 53 different conditions during the study period. Chlamydia was the most common condition, followed by hepatitis B, hepatitis C, and gonorrhea. Automated electronic laboratory reporting identified 4.4 times as many cases as traditional spontaneous, paper-based methods and identified those cases 7.9 days earlier than spontaneous reporting. CONCLUSIONS: Automated electronic laboratory reporting improves the completeness and timeliness of disease surveillance, which will enhance public health awareness and reporting efficiency.

A systematic comparison of contemporary automatic speech recognition engines for conversational clinical speech.

Conversations especially between a clinician and a patient are important sources of data to support clinical care. To date, clinicians act as the sensor to capture these data and record them in the medical record. Automatic speech recognition (ASR) engines have advanced to support continuous speech, to work independently of speaker and deliver continuously improving performance. Near human levels of performance have been reported for several ASR engines. We undertook a systematic comparison of selected ASRs for clinical conversational speech. Using audio recorded from unscripted clinical scenarios using two microphones, we evaluated eight ASR engines using word error rate (WER) and the precision, recall and F1 scores for concept extraction. We found a wide range of word errors across the ASR engines, with values ranging from 65% to 34%, all falling short of the rates achieved for other conversational speech. Recall for health concepts also ranged from 22% to 74%. Concept recall rates match or exceed expectations given measured word error rates suggesting that vocabulary is not the dominant issue.