Faculty Bibliography

OBJECTIVE:We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout. MATERIALS AND METHODS:We used EHR metadata from January to May, 2021 for 130 079 ambulatory physician Epic users. We identified cohorts of physicians who decreased note length and/or documentation time and analyzed changes in their note composition. RESULTS:37 857 physicians decreased either note length (n = 15 647), time in notes (n = 15 417), or both (n = 6793). Note length decreases were primarily attributable to reductions in copy/paste text (average relative change of -18.9%) and templated text (-17.2%). Note time decreases were primarily attributable to reductions in manual text (-27.3%) and increases in note content from other care team members (+21.1%). DISCUSSION:Organizations must consider priorities and tradeoffs in the distinct approaches needed to address different contributors to EHR burden. CONCLUSION:Future research should explore scalable burden-reduction initiatives responsive to both note bloat and documentation time.

OBJECTIVES:Social determinants have an outsized impact on health outcomes. Given the increasing awareness of this impact and the adoption of alternative payment models that incentivize addressing social needs, expectations are growing that health systems will appropriately screen for patients' social needs. However, there is limited evidence on how patients would like their health systems to engage with them around these needs. Our objective was to understand patient perspectives on completing social needs screening through technology-based modalities. STUDY DESIGN:We performed a qualitative study with semistructured patient interviews from November 2021 to April 2022. METHODS:Patients were eligible for our health system's standardized social needs screening survey if they had not completed it in the past year and were scheduled for a nonacute primary care visit. Patients were selected for interview if they completed the survey via portal or tablet or if they were eligible for but did not complete the survey. Interviews were analyzed using an integrated approach. Domains, subdomains, and themes were identified. RESULTS:We completed interviews with 54 participants. Participants were broadly accepting of screening, and most were comfortable with portal or tablet-based screening. They were motivated to complete the screening and recognized the connection between social needs and health. Having a trusting relationship with their clinician and feeling that their information was private were noted by patients as important factors for process endorsement. CONCLUSIONS:This qualitative study provides insight into patient-centered approaches for identifying patients' social needs.

Expansive growth in the use of health information technology (HIT) has dramatically altered medicine without translating to fully realized improvements in healthcare delivery. Bridging this divide will require healthcare professionals with all levels of expertise in clinical informatics. However, due to scarce opportunities for exposure and training in informatics, medical students remain an underdeveloped source of potential informaticists. To address this gap, our institution developed and implemented a 5-tiered clinical informatics curriculum at the undergraduate medical education level: (1) a practical orientation to HIT for rising clerkship students; (2) an elective for junior students; (3) an elective for senior students; (4) a longitudinal area of concentration; and (5) a yearlong predoctoral fellowship in operational informatics at the health system level. Most students found these offerings valuable for their training and professional development. We share lessons and recommendations for medical schools and health systems looking to implement similar opportunities.

This cross-sectional study assesses the association between patient characteristics and use of different devices to access telehealth visits during the COVID-19 pandemic.

BACKGROUND:Falls are the most common cause of injury-related death for patients older than 45.  We hypothesized that a machine learning algorithm developed from state-level registry data could make accurate outcome predictions at a level 1 trauma hospital. METHODS:Data for all patients admitted for fall injury during 2009 - 2019 in the state of Pennsylvania were derived from the state trauma registry.  Thirteen variables that were immediately available upon patient arrival were used for prediction modeling.  Data for the test institution were withheld from model creation.  Algorithms assessed included logistic regression (LR), random forest (RF), and extreme gradient boost (XGB).  Model discrimination for mortality was assessed with area under the curve (AUC) for each algorithm at our level 1 trauma center. RESULTS:180,284 patients met inclusion criteria.  The mean age was 69 years ± 18.5 years with a mortality rate of 4.0%.  The AUC for predicting mortality in patients that fall for LR, RF, and XGB were 0.797, 0.876, and 0.880, respectively.  The variables which contributed to the prediction in descending order of importance for XGB were respiratory rate, pulse, systolic blood pressure, ethnicity, weight, sex, age, temperature, Glasgow Coma Scale (GCS) eye, race, GCS voice, GCS motor, and blood alcohol level. CONCLUSIONS:An extreme gradient boost model developed using state-wide trauma data can accurately predict mortality after fall at a single center within the state.  This machine learning model can be implemented by local trauma systems within the state of Pennsylvania to identify patients injured by fall that require greater attention, transfer to a higher level of care, and higher resource allocation.

PURPOSE OF REVIEW:Behavioral economics represents a promising set of principles to inform the design of health-promoting interventions. Techniques from the field have the potential to increase quality of cardiovascular care given suboptimal rates of guideline-directed care delivery and patient adherence to optimal health behaviors across the spectrum of cardiovascular care delivery. RECENT FINDINGS:Cardiovascular health-promoting interventions have demonstrated success in using a wide array of principles from behavioral economics, including loss framing, social norms, and gamification. Such approaches are becoming increasingly sophisticated and focused on clinical cardiovascular outcomes in addition to health behaviors as a primary endpoint. Many approaches can be used to improve patient decisions remotely, which is particularly useful given the shift to virtual care in the context of the COVID-19 pandemic. Numerous applications for behavioral economics exist in the cardiovascular care delivery space, though more work is needed before we will have a full understanding of ways to best leverage such applications in each clinical context.

This cohort study examines whether there is an association between primary care appointment times and statin prescribing rates for patients with elevated risk of major adverse cardiovascular events.

BACKGROUND:As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model based on immediately available admission data. Secondary objectives were to predict who would be discharged home and determine which variables had the largest effect on prediction. METHODS:All patients who were admitted for fall between 2012 and 2017 at our level 1 trauma center were reviewed. Fourteen variables describing patient demographics, injury characteristics, and physiology were collected at the time of admission and were used for prediction modeling. Algorithms assessed included LR, decision tree classifier (DTC), and random forest classifier (RFC). Area under the receiver operating characteristic curve (AUC) values were calculated for each algorithm for mortality and discharge to home. RESULTS:About 4725 patients met inclusion criteria. The mean age was 61 ± 20.5 y, Injury Severity Score 8 ± 7, length of stay 5.8 ± 7.6 d, intensive care unit length of stay 1.8± 5.2 d, and ventilator days 0.7 ± 4.2 d. The mortality rate was 3% and three times greater for elderly (aged 65 y and older) patients (5.0% versus 1.6%, P < 0.001). The AUC for predicting mortality for LR, DTC, and RFC was 0.78, 0.64, and 0.86, respectively. The AUC for predicting discharge to home for LR, DTC, and RFC was 0.72, 0.61, and 0.74, respectively. The top five variables that contribute to the prediction of mortality in descending order of importance are the Glasgow Coma Score (GCS) motor, GCS verbal, respiratory rate, GCS eye, and temperature. CONCLUSIONS:RFC can accurately predict mortality and discharge home after a fall. This predictive model can be implemented at the time of patient arrival and may help identify candidates for targeted intervention as well as improve prognostication and resource utilization.

PURPOSE OF REVIEW/OBJECTIVE:Hypertension is common, impacting an estimated 108 million US adults, and deadly, responsible for the deaths of one in six adults annually. Optimal management includes frequent blood pressure monitoring and antihypertensive medication titration, but in the traditional office-based care delivery model, patients have their blood pressure measured only intermittently and in a way that is subject to misdiagnosis with white coat or masked hypertension. There is a growing opportunity to leverage our expanding repository of digital technology to reimagine hypertension care delivery. This paper reviews existing and emerging digital tools available for hypertension management, as well as behavioral economic insights that could supercharge their impact. RECENT FINDINGS/RESULTS:Digitally connected blood pressure monitors offer an alternative to office-based blood pressure monitoring. A number of cuffless blood pressure monitors are in development but require further validation before they can be deployed for widespread clinical use. Patient-facing hubs and applications offer a means to transmit blood pressure data to clinicians. Though artificial intelligence could allow for curation of this data, its clinical use for hypertension remains limited to assessing risk factors at this time. Finally, text-based and telemedicine platforms are increasingly being employed to translate hypertension data into clinical outcomes with promising results. SUMMARY/CONCLUSIONS:The digital management of hypertension shows potential as an avenue for increasing patient engagement and improving clinical efficiency and outcomes. It is important for clinicians to understand the benefits, limitations, and future directions of digital health to optimize management of hypertension.