Toward Alleviating Clinician Documentation Burden: A Scoping Review of Burden Reduction Efforts.

BACKGROUND: Studies have shown that documentation burden experienced by clinicians may lead to less direct patient care, increased errors, and job dissatisfaction. Implementing effective strategies within health care systems to mitigate documentation burden can result in improved clinician satisfaction and more time spent with patients. However, there is a gap in the literature regarding evidence-based interventions to reduce documentation burden. OBJECTIVES: The objective of this review was to identify and comprehensively summarize the state of the science related to documentation burden reduction efforts. METHODS: Following Joanna Briggs Institute Manual for Evidence Synthesis and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we conducted a comprehensive search of multiple databases, including PubMed, Medline, Embase, CINAHL Complete, Scopus, and Web of Science. Additionally, we searched gray literature and used Google Scholar to ensure a thorough review. Two reviewers independently screened titles and abstracts, followed by full-text review, with a third reviewer resolving any discrepancies. Data extraction was performed and a table of evidence was created. RESULTS: A total of 34 articles were included in the review, published between 2016 and 2022, with a majority focusing on the United States. The efforts described can be categorized into medical scribes, workflow improvements, educational interventions, user-driven approaches, technology-based solutions, combination approaches, and other strategies. The outcomes of these efforts often resulted in improvements in documentation time, workflow efficiency, provider satisfaction, and patient interactions. CONCLUSION: This scoping review provides a comprehensive summary of health system documentation burden reduction efforts. The positive outcomes reported in the literature emphasize the potential effectiveness of these efforts. However, more research is needed to identify universally applicable best practices, and considerations should be given to the transfer of burden among members of the health care team, quality of education, clinician involvement, and evaluation methods.

Differences in Care Team Response to Patient Portal Messages by Patient Race and Ethnicity.

Importance: The COVID-19 pandemic was associated with substantial growth in patient portal messaging. Higher message volumes have largely persisted, reflecting a new normal. Prior work has documented lower message use by patients who belong to minoritized racial and ethnic groups, but research has not examined differences in care team response to messages. Both have substantial ramifications on resource allocation and care access under a new care paradigm with portal messaging as a central channel for patient-care team communication. Objective: To examine differences in how care teams respond to patient portal messages sent by patients from different racial and ethnic groups. Design, Setting, and Participants: In a cross-sectional design in a large safety-net health system, response outcomes from medical advice message threads sent from January 1, 2021, through November 24, 2021, from Asian, Black, Hispanic, and White patients were compared, controlling for patient and message thread characteristics. Asian, Black, Hispanic, and White patients with 1 or more adult primary care visits at Boston Medical Center in calendar year 2020 were included. Data analysis was conducted from June 23, 2022, through December 21, 2023. Exposure: Patient race and ethnicity. Main Outcomes and Measures: Rates at which medical advice request messages were responded to by care teams and the types of health care professionals that responded. Results: A total of 39 043 patients were included in the sample: 2006 were Asian, 21 600 were Black, 7185 were Hispanic, and 8252 were White. A total of 22 744 (58.3%) patients were women and mean (SD) age was 50.4 (16.7) years. In 2021, these patients initiated 57 704 medical advice request message threads. When patients who belong to minoritized racial and ethnic groups sent these messages, the likelihood of receiving any care team response was similar, but the types of health care professionals that responded differed. Black patients were 3.95 percentage points (pp) less likely (95% CI, -5.34 to -2.57 pp; P < .001) to receive a response from an attending physician, and 3.01 pp more likely (95% CI, 1.76-4.27 pp; P < .001) to receive a response from a registered nurse, corresponding to a 17.4% lower attending response rate. Similar, but smaller, differences were observed for Asian and Hispanic patients. Conclusions and Relevance: The findings of this study suggest lower prioritization of patients who belong to minoritized racial and ethnic groups during triaging. Understanding and addressing these disparities will be important for improving care equity and informing health care delivery support algorithms.

Cumulated time to chart closure: a novel electronic health record-derived metric associated with clinician burnout.

Objective: We sought to determine whether average cumulated time to chart closure (CTCC), a novel construct to measure clinician workload burden, and electronic health record (EHR) measures were associated with a validated measure of burnout. Materials and methods: Physicians at a large academic institution participated in a well-being survey that was linked to their EHR use data. CTCC was defined as the average time from the start of patient encounters to chart closure over a set of encounters. Established EHR use measures including daily total time in the EHR (EHR-Time8), time in the EHR outside scheduled hours, work outside of work (WOW8), and time spent on inbox (IB-Time8) were calculated. We examined the relationship between CTCC, EHR use metrics, and burnout using descriptive statistics and adjusted logistic regression models. Results: We included data from 305 attendings, encompassing 242 432 ambulatory encounters (2021). Among them, 42% (128 physicians) experienced burnout. The median CTCC for all clinicians was 32.5 h. Unadjusted analyses revealed significant associations between CTCC, WOW8, IB-Time8, and burnout. In a final adjusted model, only CTCC remained statistically significant with an odds ratio estimate of 1.42 (95% CI, 1.00-2.01). Discussion: These results suggest that CTCC is predictive of burnout and that purely measuring duration of interaction with the EHR itself is not sufficient to capture burnout. Conclusion: Workload burden as manifested by average CTCC has the potential to be a practical, quantifiable measure that will allow for identification of clinicians at risk of burnout and to assess the success of interventions designed to address burnout.

Identifying and Addressing Barriers to Implementing Core Electronic Health Record Use Metrics for Ambulatory Care: Virtual Consensus Conference Proceedings.

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS: We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care. CONCLUSION: Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop's outputs offers promise for overcoming barriers to implementing EHR use metrics.

Electronic Health Record Adolescent Confidentiality in a Safety Net Setting.

BACKGROUND: The 21st Century Cures Act provides improved access to one's medical record but presents new challenges to adolescent confidentiality in the domain of health care information technology. Adolescent patients, who have the right to confidential care in certain areas as dictated by state law, face the prospect of parents and guardians erroneously accessing their confidential health information. OBJECTIVES: Our institution, the largest safety net hospital in our region, sought to quantify the proportion of adolescent patient portal accounts likely being accessed by guardians and to implement corrective measures for the patient portal sign-up process in our electronic health record (EHR) system. METHODS: We manually reviewed our institution's adolescent patient portal accounts based on email addresses associated with adolescents' accounts. We implemented EHR changes to address the potential for breach of confidentiality as a result of adolescent patient portal account creation by guardians. One intervention was creating a "guardrail rule" to prevent guardians from creating adolescent patient portal accounts with their own email addresses. After email reminders to adolescent patients to update their accounts, we deactivated accounts with concern for erroneous guardian access. RESULTS: Sixty percent of our institution's adolescent patient portal accounts had possible direct access by guardians. Following requests to update account access, 425 (18.8%) of accounts had email addresses updated by adolescent patients and 1,830 (81.2%) accounts were deactivated. CONCLUSION: More nuanced EHR functionality for adolescent patients and their guardians can help health care systems provide confidential, patient-centered care for adolescents, while allowing guardians to access appropriate health information to facilitate care. There is an opportunity for a national consensus on how adolescents and their guardians can access health information by patient portal.

Telehealth Policy, Practice, and Education: a Position Statement of the Society of General Internal Medicine.

Telehealth services, specifically telemedicine audio-video and audio-only patient encounters, expanded dramatically during the COVID-19 pandemic through temporary waivers and flexibilities tied to the public health emergency. Early studies demonstrate significant potential to advance the quintuple aim (patient experience, health outcomes, cost, clinician well-being, and equity). Supported well, telemedicine can particularly improve patient satisfaction, health outcomes, and equity. Implemented poorly, telemedicine can facilitate unsafe care, worsen disparities, and waste resources. Without further action from lawmakers and agencies, payment will end for many telemedicine services currently used by millions of Americans at the end of 2024. Policymakers, health systems, clinicians, and educators must decide how to support, implement, and sustain telemedicine, and long-term studies and clinical practice guidelines are emerging to provide direction. In this position statement, we use clinical vignettes to review relevant literature and highlight where key actions are needed. These include areas where telemedicine must be expanded (e.g., to support chronic disease management) and where guidelines are needed (e.g., to prevent inequitable offering of telemedicine services and prevent unsafe or low-value care). We provide policy, clinical practice, and education recommendations for telemedicine on behalf of the Society of General Internal Medicine. Policy recommendations include ending geographic and site restrictions, expanding the definition of telemedicine to include audio-only services, establishing appropriate telemedicine service codes, and expanding broadband access to all Americans. Clinical practice recommendations include ensuring appropriate telemedicine use (for limited acute care situations or in conjunction with in-person services to extend longitudinal care relationships), that the choice of modality be done through patient-clinician shared decision-making, and that health systems design telemedicine services through community partnerships to ensure equitable implementation. Education recommendations include developing telemedicine-specific educational strategies for trainees that align with accreditation body competencies and providing educators with protected time and faculty development resources.

Personalized hypertension treatment recommendations by a data-driven model.

BACKGROUND: Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. METHODS: Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. RESULTS: The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. CONCLUSION: Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.

Performance of three screening tools to predict COVID-19 positivity in emergency department patients.

BACKGROUND: COVID-19 symptoms vary widely. This retrospective study assessed which of three clinical screening tools-a nursing triage screen (NTS), an ED review of systems (ROS) performed by physicians and physician assistants and a standardised ED attending (ie, consultant) physician COVID-19 probability assessment (PA)-best identified patients with COVID-19 on a subsequent reverse transcription PCR (RT-PCR) confirmation. METHODS: All patients admitted to Boston Medical Center from the ED between 27 April 2020 and 17 May 2020 were included. Sensitivity, specificity and positive predictive value (PPV) and negative predictive value (NPV) were calculated for each method. Logistic regression assessed each tool's performance. RESULTS: The attending physician PA had higher sensitivity (0.62, 95% CI 0.53 to 0.71) than the NTS (0.46, 95% CI 0.37 to 0.56) and higher specificity (0.76, 95% CI 0.72 to 0.80) than the NTS (0.71, 95% CI 0.66 to 0.75) and ED ROS (0.62, 95% CI 0.58 to 0.67). Categorisation as moderate or high probability on the ED physician PA was associated with the highest odds of having COVID-19 in regression analyses (adjusted OR=4.61, 95% CI 3.01 to 7.06). All methods had a low PPV (ranging from 0.26 for the ED ROS to 0.40 for the attending physician PA) and a similar NPV (0.84 for both the NTS and the ED ROS, and 0.89 for the attending physician PA). CONCLUSION: The ED attending PA had higher sensitivity and specificity than the other two methods, but none was accurate enough to replace a COVID-19 RT-PCR test in a clinical setting where transmission control is crucial. Therefore, we recommend universal COVID-19 testing prior to all admissions.

Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population.

OBJECTIVE: To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs. MATERIALS AND METHODS: Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models. RESULTS: Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively. DISCUSSION: The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories. CONCLUSIONS: This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.

Evaluation of mHealth Apps for Diverse, Low-Income Patient Populations: Framework Development and Application Study.

BACKGROUND: The use of mobile technology or smartphones has grown exponentially in the United States, allowing more individuals than ever internet access. This access has been especially critical to households earning less than US $30,000, the majority of whom indicate that smartphones are their main source of internet access. The increasing ubiquity of smartphones and virtual care promises to offset some of the health disparities that cut through the United States. However, disparities cannot be addressed if the medical information offered though smartphones is not accessible or reliable. OBJECTIVE: This study seeks to create a framework to review the strengths and weaknesses of mobile Health (mHealth) apps for diverse, low-income populations. METHODS: Focusing on smoking cessation, diabetes management, and medication adherence as models of disease management, we describe the process for selecting, evaluating, and obtaining patient feedback on mHealth apps. RESULTS: The top 2 scoring apps in each category were QuitNow! and Smoke Free-Quit Smoking Now for smoking cessation, Glucosio and MyNetDiary for diabetes management, and Medisafe and MyMeds for medication adherence. CONCLUSIONS: We believe that this framework will prove useful for future mHealth app development, and clinicians and patient advisory groups in connecting culturally, educationally, and socioeconomically appropriate mHealth apps with low-income, diverse communities and thus work to bridge health disparities.

Rapid Cycle Evaluation and Adaptation of an Inpatient Tobacco Treatment Service at a U.S. Safety-Net Hospital.

Background: To address disparities in smoking rates, our safety-net hospital implemented an inpatient tobacco treatment intervention: an "opt-out" electronic health record (EHR)-based Best Practice Alert + order-set, which triggers consultation to a Tobacco Treatment Consult (TTC) service for all hospitalized patients who smoke cigarettes. We report on development, implementation, and adaptation of the intervention, informed by a pre-implementation needs assessment and two rapid-cycle evaluations guided by the Consolidated Framework for Implementation Research (CFIR) and Expert Recommendations for Implementing Change (ERIC) compilation. Methods: We identified stakeholders affected by implementation and conducted a local needs assessment starting 6 months-pre-launch. We then conducted two rapid-cycle evaluations during the first 6 months post-implementation. The CFIR informed survey and interview guide development, data collection, assessment of barriers and facilitators, and selection of ERIC strategies to implement and adapt the intervention. Results: Key themes were: (1) Understanding the hospital's priority to improving tobacco performance metrics was critical in gaining leadership buy-in (CFIR Domain: Outer setting; Construct: External Policy and Incentives). (2) CFIR-based rapid-cycle evaluations allowed us to recognize implementation challenges early and select ERIC strategies clustering into 3 broad categories (conducting needs assessment; developing stakeholder relationships; training and educating stakeholders) to make real-time adaptations, creating an acceptable clinical workflow. (3) Minimizing clinician burden allowed the successful implementation of the TTC service. (4) Demonstrating improved 6-month quit rates and tobacco performance metrics were key to sustaining the program. Conclusions: Rapid-cycle evaluations to gather pre-implementation and early-implementation data, focusing on modifiable barriers and facilitators, allowed us to develop and refine the intervention to improve acceptability, adoption, and sustainability, enabling us to improve tobacco performance metrics in a short timeline. Future directions include spreading rapid-cycle evaluations to promote implementation of inpatient tobacco treatment programs to other settings and assessing long-term sustainability and return on investment of these programs.

Impact of Clinical Decision Support on Antibiotic Prescribing for Acute Respiratory Infections: a Cluster Randomized Implementation Trial.

BACKGROUND: Clinical decision support (CDS) is a promising tool for reducing antibiotic prescribing for acute respiratory infections (ARIs). OBJECTIVE: To assess the impact of previously effective CDS on antibiotic-prescribing rates for ARIs when adapted and implemented in diverse primary care settings. DESIGN: Cluster randomized clinical trial (RCT) implementing a CDS tool designed to guide evidence-based evaluation and treatment of streptococcal pharyngitis and pneumonia. SETTING: Two large academic health system primary care networks with a mix of providers. PARTICIPANTS: All primary care practices within each health system were invited. All providers within participating clinic were considered a participant. Practices were randomized selection to a control or intervention group. INTERVENTIONS: Intervention practice providers had access to an integrated clinical prediction rule (iCPR) system designed to determine the risk of bacterial infection from reason for visit of sore throat, cough, or upper respiratory infection and guide evidence-based evaluation and treatment. MAIN OUTCOME(S): Change in overall antibiotic prescription rates. MEASURE(S): Frequency, rates, and type of antibiotics prescribed in intervention and controls groups. RESULTS: 33 primary care practices participated with 541 providers and 100,573 patient visits. Intervention providers completed the tool in 6.9% of eligible visits. Antibiotics were prescribed in 35% and 36% of intervention and control visits, respectively, showing no statistically significant difference. There were also no differences in rates of orders for rapid streptococcal tests (RR, 0.94; P = 0.11) or chest X-rays (RR, 1.01; P = 0.999) between groups. CONCLUSIONS: The iCPR tool was not effective in reducing antibiotic prescription rates for upper respiratory infections in diverse primary care settings. This has implications for the generalizability of CDS tools as they are adapted to heterogeneous clinical contexts. TRIAL REGISTRATION: Clinicaltrials.gov (NCT02534987). Registered August 26, 2015 at https://clinicaltrials.gov.

Race/Ethnicity, Underlying Medical Conditions, Homelessness, and Hospitalization Status of Adult Patients with COVID-19 at an Urban Safety-Net Medical Center - Boston, Massachusetts, 2020.

As of July 5, 2020, approximately 2.8 million coronavirus disease 2019 (COVID-19) cases and 130,000 COVID-19-associated deaths had been reported in the United States (1). Populations historically affected by health disparities, including certain racial and ethnic minority populations, have been disproportionally affected by and hospitalized with COVID-19 (2-4). Data also suggest a higher prevalence of infection with SARS-CoV-2, the virus that causes COVID-19, among persons experiencing homelessness (5). Safety-net hospitals,† such as Boston Medical Center (BMC), which provide health care to persons regardless of their insurance status or ability to pay, treat higher proportions of these populations and might experience challenges during the COVID-19 pandemic. This report describes the characteristics and clinical outcomes of adult patients with laboratory-confirmed COVID-19 treated at BMC during March 1-May 18, 2020. During this time, 2,729 patients with SARS-CoV-2 infection were treated at BMC and categorized into one of the following mutually exclusive clinical severity designations: exclusive outpatient management (1,543; 56.5%), non-intensive care unit (ICU) hospitalization (900; 33.0%), ICU hospitalization without invasive mechanical ventilation (69; 2.5%), ICU hospitalization with mechanical ventilation (119; 4.4%), and death (98; 3.6%). The cohort comprised 44.6% non-Hispanic black (black) patients and 30.1% Hispanic or Latino (Hispanic) patients. Persons experiencing homelessness accounted for 16.4% of patients. Most patients who died were aged ≥60 years (81.6%). Clinical severity differed by age, race/ethnicity, underlying medical conditions, and homelessness. A higher proportion of Hispanic patients were hospitalized (46.5%) than were black (39.5%) or non-Hispanic white (white) (34.4%) patients, a finding most pronounced among those aged <60 years. A higher proportion of non-ICU inpatients were experiencing homelessness (24.3%), compared with homeless patients who were admitted to the ICU without mechanical ventilation (15.9%), with mechanical ventilation (15.1%), or who died (15.3%). Patient characteristics associated with illness and clinical severity, such as age, race/ethnicity, homelessness, and underlying medical conditions can inform tailored strategies that might improve outcomes and mitigate strain on the health care system from COVID-19.

Effectiveness of an Opt-Out Electronic Heath Record-Based Tobacco Treatment Consult Service at an Urban Safety Net Hospital.

BACKGROUND: To address the burden of tobacco use in underserved populations, our safety net hospital developed a tobacco treatment intervention consisting of an "opt-out" electronic health record-based best practice alert + order set, which triggers consultation to an inpatient tobacco treatment consult (TTC) service for all hospitalized smokers. RESEARCH QUESTION: We sought to understand if the intervention would increase patient-level outcomes (receipt of tobacco treatment during hospitalization and at discharge; 6-month smoking abstinence) and improve hospital-wide performance on tobacco treatment metrics. DESIGN AND METHODS: We conducted two retrospective quasi-experimental analyses to examine effectiveness of the TTC service. Using a pragmatic design and multivariable logistic regression, we compared patient-level outcomes of receipt of nicotine replacement therapy and 6-month quit rates between smokers seen by the service (n = 505) and eligible smokers not seen because of time constraints (n = 680) between July 2016 and December 2016. In addition, we conducted an interrupted time series analysis to examine the effect of the TTC service on hospital-level performance measures, comparing reported Joint Commission measure rates for inpatient (Tob-2) and postdischarge (Tob-3) tobacco treatment preimplementation (January 2015-June 2016) vs postimplementation (July 2016-December 2017) of the intervention. RESULTS: Compared with inpatient smokers not seen by the TTC service, smokers seen by the TTC service had higher odds of receiving nicotine replacement during hospitalization (260 of 505 [51.5%] vs 244 of 680 [35.9%]; adjusted ORs [AOR], 1.93 [95% CI, 1.5-2.45]) and at discharge (164 of 505 [32.5%] vs 84 of 680 [12.4%]; AOR, 3.41 [95% CI, 2.54-4.61]), as well as higher odds of 6-month smoking abstinence (75 of 505 [14.9%] vs 68 of 680 [10%]; AOR, 1.48 [95% CI, 1.03-2.12]). Hospital-wide, the intervention was associated with a change in slope trends for Tob-3 (P < .01), but not for Tob-2. INTERPRETATION: The "opt-out" electronic health record-based TTC service at our large safety net hospital was effective at improving both patient-level outcomes and hospital-level performance metrics, and could be implemented at other safety net hospitals that care for hard-to-reach smokers.

Using normalisation process theory to understand workflow implications of decision support implementation across diverse primary care settings.

BACKGROUND: Effective implementation of technologies into clinical workflow is hampered by lack of integration into daily activities. Normalisation process theory (NPT) can be used to describe the kinds of 'work' necessary to implement and embed complex new practices. We determined the suitability of NPT to assess the facilitators, barriers and 'work' of implementation of two clinical decision support (CDS) tools across diverse care settings. METHODS: We conducted baseline and 6-month follow-up quantitative surveys of clinic leadership at two academic institutions' primary care clinics randomised to the intervention arm of a larger study. The survey was adapted from the NPT toolkit, analysing four implementation domains: sense-making, participation, action, monitoring. Domains were summarised among completed responses (n=60) and examined by role, institution, and time. RESULTS: The median score for each NPT domain was the same across roles and institutions at baseline, and decreased at 6 months. At 6 months, clinic managers' participation domain (p=0.003), and all domains for medical directors (p<0.003) declined. At 6 months, the action domain decreased among Utah respondents (p=0.03), and all domains decreased among Wisconsin respondents (p≤0.008). CONCLUSIONS: This study employed NPT to longitudinally assess the implementation barriers of new CDS. The consistency of results across participant roles suggests similarities in the work each role took on during implementation. The decline in engagement over time suggests the need for more frequent contact to maintain momentum. Using NPT to evaluate this implementation provides insight into domains which can be addressed with participants to improve success of new electronic health record technologies. TRIAL REGISTRATION NUMBER: NCT02534987.

Implementing an EHR-based Screening and Referral System to Address Social Determinants of Health in Primary Care.

BACKGROUND: Social determinants affect health, yet there are few systematic clinical strategies in primary care that leverage electronic health record (EHR) automation to facilitate screening for social needs and resource referrals. An EHR-based social determinants of health (SDOH) screening and referral model, adapted from the WE CARE model for pediatrics, was implemented in urban adult primary care. OBJECTIVES: This study aimed to: (1) understand the burden of SDOH among patients at Boston Medical Center; and (2) evaluate the feasibility of implementing a systematic clinical strategy to screen new primary care patients for SDOH, use EHR technology to add these needs to the patient's chart through autogenerated ICD-10 codes, and print patient language-congruent referrals to available resources upon patient request. RESEARCH DESIGN: This observational study assessed the number of patients who were screened to be positive and requested resources for social needs. In addition, we evaluated the feasibility of implementing our SDOH strategy by determining the proportion of: eligible patients screened, providers signing orders for positive patient screenings, and provider orders for resource referral guides among patients requesting resource connections. RESULTS: In total, 1696 of 2420 (70%) eligible patients were screened. Employment (12%), food insecurity (11%), and problems affording medications (11%) were the most prevalent concerns among respondents. In total, 367 of 445 (82%) patients with ≥1 identified needs (excluding education) had the appropriate ICD-10 codes added to their visit diagnoses. In total, 325 of 376 (86%) patients who requested resources received a relevant resource referral guide. CONCLUSIONS: Implementing a systematic clinical strategy in primary care using EHR workflows was successful in identifying and providing resource information to patients with SDOH needs.