Understanding social needs screening and demographic data collection in primary care practices serving Maryland Medicare patients.

BACKGROUND: Health outcomes are strongly impacted by social determinants of health, including social risk factors and patient demographics, due to structural inequities and discrimination. Primary care is viewed as a potential medical setting to assess and address individual health-related social needs and to collect detailed patient demographics to assess and advance health equity, but limited literature evaluates such processes. METHODS: We conducted an analysis of cross-sectional survey data collected from n = 507 Maryland Primary Care Program (MDPCP) practices through Care Transformation Requirements (CTR) reporting in 2022. Descriptive statistics were used to summarize practice responses on social needs screening and demographic data collection. A stepwise regression analysis was conducted to determine factors predicting screening of all vs. a targeted subset of beneficiaries for unmet social needs. RESULTS: Almost all practices (99%) reported conducting some form of social needs screening and demographic data collection. Practices reported variation in what screening tools or demographic questions were employed, frequency of screening, and how information was used. More than 75% of practices reported prioritizing transportation, food insecurity, housing instability, financial resource strain, and social isolation. CONCLUSIONS: Within the MDPCP program there was widespread implementation of social needs screenings and demographic data collection. However, there was room for additional supports in addressing some challenging social needs and increasing detailed demographics. Further research is needed to understand any adjustments to clinical care in response to identified social needs or application of data for uses such as assessing progress towards health equity and the subsequent impact on clinical care and health outcomes.

Early mortality benefit with COVID-19 convalescent plasma: a matched control study.

Convalescent plasma can provide passive immunity during viral outbreaks, but the benefit is uncertain for the treatment of novel coronavirus disease 2019 (COVID-19). Our goal is to assess the efficacy of COVID-19 convalescent plasma (CCP). In all, 526 hospitalized patients with laboratory-confirmed SARS-CoV-2 at an academic health system were analyzed. Among them, 263 patients received CCP and were compared to 263 matched controls with standard treatment. The primary outcome was 28-day mortality with a subanalysis at 7 and 14 days. No statistical difference in 28-day mortality was seen in CCP cases (25·5%) compared to controls (27%, P = 0·06). Seven-day mortality was statistically better for CCP cases (9·1%) than controls (19·8%, P < 0·001) and continued at 14 days (14·8% vs. 23·6%, P = 0·01). After 72 h, CCP transfusion resulted in transitioning from nasal cannula to room air (median 4 days vs. 1 day, P = 0·02). The length of stay was longer in CCP cases than controls (14·3 days vs. 11·4 days, P < 0·001). Patients with COVID-19 who received CCP had a decreased risk of death at 7 and 14 days, but not 28 days after transfusion. To date, this is the largest study demonstrating a mortality benefit for the use of CCP in patients with COVID-19 compared to matched controls.

The Radiologist's Gaze: Mapping Three-Dimensional Visual Search in Computed Tomography of the Abdomen and Pelvis.

A radiologist's search pattern can directly influence patient management. A missed finding is a missed opportunity for intervention. Multiple studies have attempted to describe and quantify search patterns but have mainly focused on chest radiographs and chest CTs. Here, we describe and quantify the visual search patterns of 17 radiologists as they scroll through 6 CTs of the abdomen and pelvis. Search pattern tracings varied among individuals and remained relatively consistent per individual between cases. Attendings and trainees had similar eye metric statistics with respect to time to first fixation (TTFF), number of fixations in the region of interest (ROI), fixation duration in ROI, mean saccadic amplitude, or total number of fixations. Attendings had fewer numbers of fixations per second versus trainees (p < 0.001), suggesting efficiency due to expertise. In those cases that were accurately interpreted, TTFF was shorter (p = 0.04), the number of fixations per second and number of fixations in ROI were higher (p = 0.04, p = 0.02, respectively), and fixation duration in ROI was increased (p = 0.02). We subsequently categorized radiologists as "scanners" or "drillers" by both qualitative and quantitative methods and found no differences in accuracy with most radiologists being categorized as "drillers." This study describes visual search patterns of radiologists in interpretation of CTs of the abdomen and pelvis to better approach future endeavors in determining the effects of manipulations such as fatigue, interruptions, and computer-aided detection.

Diagnostic accuracy of a rapid telemedicine encounter in the Emergency Department.

OBJECTIVES: Emergency Department crowding is an increasing problem, leading to treatment delays and higher risk of mortality. Our institution recently implemented a telemedicine physician intake ("tele-intake") process as a mitigating front-end strategy. Previous studies have focused on ED throughput metrics such as door to disposition; our work aimed to specifically assess the tele-intake model for clinical accuracy. METHODS: We retrospectively reviewed ED visits at a high acuity, tertiary care academic hospital before and after tele-intake implementation. We defined the primary outcome as the degree of additional laboratory, imaging, and medication orders placed by the subsequent ED provider. Our secondary outcomes were the cancellation rate of intake orders and the percentage of encounters where no additional second provider orders were necessary. RESULTS: For in-person and tele-intake physician encounters between September 2015 and February 2017, most labs and diagnostic radiology studies, and approximately half of CT, ultrasound, and pharmacy orders were initiated by the intake physician. We found no significant difference for our primary outcome (p = 0.2449). For both tele-intake and in-person encounters, <1% of orders were cancelled by the second provider. Additionally, 30.8% of in-person and 31.5% of telemedicine patient encounters required no additional orders to make a disposition decision. DISCUSSION: This novel analysis of an innovative patient care model suggests that the benefits of tele-intake as a replacement for in-person physician directed intake are not at the cost of over or under utilization of diagnostic testing or interventions.

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study.

BACKGROUND: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. OBJECTIVE: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration's postmarket surveillance capabilities while minimizing public burden. This study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach. METHODS: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV. RESULTS: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively. CONCLUSIONS: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection.

Multilevel approaches to address disparities in lung cancer screening: a study protocol.

BACKGROUND: Low-dose computed tomography (lung cancer screening) can reduce lung cancer-specific mortality by 20-24%. Based on this evidence, the United States Preventive Services Task Force recommends annual lung cancer screening for asymptomatic high-risk individuals. Despite this recommendation, utilization is low (3-20%). Lung cancer screening may be particularly beneficial for African American patients because they are more likely to have advanced disease, lower survival, and lower screening rates compared to White individuals. Evidence points to multilevel approaches that simultaneously address multiple determinants to increase screening rates and decrease lung cancer burden in minoritized populations. This study will test the effects of provider- and patient-level strategies for promoting equitable lung cancer screening utilization. METHODS: Guided by the Health Disparities Research Framework and the Practical, Robust Implementation and Sustainability Model, we will conduct a quasi-experimental study with four primary care clinics within a large health system (MedStar Health). Individuals eligible for lung cancer screening, defined as 50-80 years old, ≥ 20 pack-years, currently smoking, or quit < 15 years, no history of lung cancer, who have an appointment scheduled with their provider, and who are non-adherent to screening will be identified via the EHR, contacted, and enrolled (N = 184 for implementation clinics, N = 184 for comparison clinics; total N = 368). Provider participants will include those practicing at the partner clinics (N = 26). To increase provider-prompted discussions about lung screening, an electronic health record (EHR) clinician reminder will be sent to providers prior to scheduled visits with the screening-eligible participants. To increase patient-level knowledge and patient activation about screening, an inreach specialist will conduct a pre-visit phone-based educational session with participants. Patient participants will be assessed at baseline and 1-week post-visit to measure provider-patient discussion, screening intentions, and knowledge. Screening referrals and screening completion rates will be assessed via the EHR at 6 months. We will use mixed methods and multilevel assessments of patients and providers to evaluate the implementation outcomes (adoption, feasibility, acceptability, and fidelity). DISCUSSION: The study will inform future work designed to measure the independent and overlapping contributions of the multilevel implementation strategies to advance equity in lung screening rates. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04675476. Registered December 19, 2020.

Making Patient Safety Event Data Actionable: Understanding Patient Safety Analyst Needs.

OBJECTIVES: The increase in patient safety reporting systems has led to the challenge of effectively analyzing these data to identify and mitigate safety hazards. Patient safety analysts, who manage reports, may be ill-equipped to make sense of report data. We sought to understand the cognitive needs of patient safety analysts as they work to leverage patient safety reports to mitigate risk and improve patient care. METHODS: Semistructured interviews were conducted with 21 analysts, from 11 hospitals across 3 healthcare systems. Data were parsed into utterances and coded to extract major themes. RESULTS: From 21 interviews, 516 unique utterances were identified and categorized into the following 4 stages of data analysis: input (15.1% of utterances), transformation (14.1%), extrapolation (30%), and output (14%). Input utterances centered on the source (35.9% of inputs) and preprocessing of data. Transformation utterances centered on recategorizing patient safety events (57.5% of transformations) or integrating external data sources (42.5% of transformations). The focus of interviews was on extrapolation and trending data (56.1% of extrapolations); alarmingly, 16.1% of trend utterances explicitly mentioned a reliance on memory. The output was either a report (56.9% of outputs) or an action (43.1% of outputs). CONCLUSIONS: Major gaps in the analysis of patient safety report data were identified. Despite software to support reporting, many reports come from other sources. Transforming data are burdensome because of recategorization of events and integration with other data sources, processes that can be automated. Surprisingly, trend identification was mostly based on patient analyst memory, highlighting a need for new tools that better support analysts.

A novel application of SMART on FHIR architecture for interoperable and scalable integration of patient-reported outcome data with electronic health records.

OBJECTIVE: Despite a proliferation of applications (apps) to conveniently collect patient-reported outcomes (PROs) from patients, PRO data are yet to be seamlessly integrated with electronic health records (EHRs) in a way that improves interoperability and scalability. We applied the newly created PRO standards from the Office of the National Coordinator for Health Information Technology to facilitate the collection and integration of standardized PRO data. A novel multitiered architecture was created to enable seamless integration of PRO data via Substitutable Medical Apps and Reusable Technologies on Fast Healthcare Interoperability Resources apps and scaled to different EHR platforms in multiple ambulatory settings. MATERIALS AND METHODS: We used a standards-based approach to deploy 2 apps that source and surface PRO data in real-time for provider use within the EHR and which rely on PRO assessments from an external center to streamline app and EHR integration. RESULTS: The apps were developed to enable patients to answer validated assessments (eg, a Patient-Reported Outcomes Measurement Information System including using a Computer Adaptive Test format). Both apps were developed to populate the EHR in real time using the Health Level Seven FHIR standard allowing providers to view patients' data during the clinical encounter. The process of implementing this architecture with 2 different apps across 18 ambulatory care sites and 3 different EHR platforms is described. CONCLUSION: Our approach and solution proved feasible, secure, and time- and resource-efficient. We offer actionable guidance for this technology to be scaled and adapted to promote adoption in diverse ambulatory care settings and across different EHRs.

Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis.

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance. We developed graphical model displays of two popular sepsis scoring systems, quick Sepsis Related Organ Failure Assessment and Predisposition, Infection, Response, Organ Failure, using human factors principles grounded in user-centered and interaction design. Models will be evaluated in a larger research effort to optimize alert design to improve the collective awareness of high-risk populations and develop a relevant point-of-care clinical decision support system for sepsis.