Faculty Bibliography

BACKGROUND: To justify nonprofit hospital organizations' tax exemption, the Affordable Care Act (ACA) requires these organizations to report on efforts to identify and invest in local health needs via Community Health Needs Assessments (CHNAs), Implementation Strategies (ISs), and Schedule H (990H) tax forms. However, there is no requirement that 990H spending aligns with topics raised on CHNAs or ISs, and recent reports have questioned whether 990H reporting categories adequately measure investments in social determinants of health. To assess the utility of ACA-mandated reporting for tracking spending on social health needs, this cross-sectional study aimed to describe how often a need identified in CHNAs is reflected in plans noted in ISs and in spending reported in 990Hs. Using housing as an example of a social health need, we focused on communities with the most homelessness to study organizations most likely to address housing.
METHOD(S): We identified nonprofit hospital organizations with facilities in the 5 metropolitan areas with highest per-capita homelessness using Department of Housing & Urban Development data and the Community Benefit Insight (CBI) database. We reviewed organizations' public reporting documents, obtained via internet search and from CBI, to determine whether they ever addressed housing on CHNAs, ISs, and 990Hs from 2015-2017. We excluded 3 organizations for which we could not obtain all 3 documents.
RESULT(S): Of 47 organizations sampled (representing 57 facilities in Washington, DC; Santa Cruz County; Boston; New York City; and San Francisco), housing was noted in 55% (n=26) of CHNAs, 36% (n=17) of ISs, and 26% (n=12) of 990Hs. Among the 26 organizations that recognized housing needs in CHNAs, 10 noted housing-related plans in ISs, and 7 reported spending on housing in 990Hs.
CONCLUSION(S): Although many nonprofit hospital organizations in areas with high homelessness recognize housing as a health need, public reporting documents provide limited evidence that an identified community need for housing was translated into related plans and spending. Further investigation should explore whether discrepancies among documented needs, strategies, and spending reflect inadequacy of the 990H for capturing housing-related spending versus hospitals' uncertainty in whether or how to invest in housing after identifying it as a health need. Regulatory reform to increase guidance for social investments and require greater concordance among CHNAs, ISs, and 990Hs could promote accountability and transparency in organizations' efforts to address housing and other health-related social needs. LEARNING OBJECTIVE #1: Quantify use of public reporting documents by nonprofit hospitals in communities with high rates of homelessness to show how they identify and invest in housing as a community health need (Patient Care) LEARNING OBJECTIVE #2: Appraise policy opportunities to enhance Affordable Care Act-mandated reporting requirements to hold nonprofit hospital organizations accountable to improving community health (SystemsBased Practice)

LEARNING OBJECTIVES 1: Characterize internal medicine resident's inpatient clinical exposure across public, private, federal and community hospital settings. LEARNING OBJECTIVES 2: Identify disparities in resident's clinical exposure across sites to guide curriculum development and rational rotation scheduling. SETTING AND PARTICIPANTS: Experiential learning through patient care is the primary means by which Internal Medicine (IM) residents mature. Despite this, there is an unmet need to characterize how residents' diverse patient care activities inform their educational experience. This is especially true at NYU's IM Residency, which comprises over 200 residents across four distinct hospital systems, exposing residents to diverse, and often variable clinical content. We have previously described a 'crosswalk tool' which maps ICD10 diagnosis codes to one of 16 American Board of Internal Medicine (ABIM) medical content areas and one of 178 specific condition categories, to better characterize clinical exposure. Here, we translate resident-attributed principal ICD-10 discharge diagnosis codes from each of our Program's four training hospitals in Quarter 1 of AY2020 to profile the educational experience of residents at each site. DESCRIPTION: From July 1-Sept 30 2020, we mined principal ICD10 discharge diagnosis codes from resident teams at Bellevue Hospital (BH), a large public hospital; NYU Langone Hospital-Brooklyn (NYU-BK), an academic community hospital; NYU Lang one Hospitals-Manhattan (NYUMN), a large quaternary hospital; and VA NY Harbor Healthcare-Manhattan (VA), a Veteran's Affairs Hospital. We then applied diagnosis codes to the crosswalk tool to translate ICD10 codes into broad ABIM content areas and specific condition categories, yielding site-specific clinical content maps. EVALUATION: At each site there was notable enrichment in two specific content areas-Infectious Disease (ID) and Cardiovascular Diseases (CVD). However, there were striking differences in the frequency of these content areas across sites. Roughly 28% of all diagnoses fell under ID at NYU-BK and NYU-MN, with half that frequency at BH and VA. CVD diagnoses represented 40% of diagnoses at VA, while only 20% at NYU-BK, 25% at NYU-MN, and 30% at BH. For reference, CVD represents 14% and ID 9% of content on theABIM Certification Exam. There were uniformly low frequencies (<1%) of several less typical ABIM content areas, namely Optho, Derm, Allergy/ Immuno, OB/GYN, and ENT/Dental Med. The frequency of Psychiatry diagnoses, which houses substance use, was markedly higher at BH and NYU-BK than the other sites. There were several substantial differences in condition categories across sites, most notably within 'bacteremia and sepsis syndromes' and 'ischemic heart disease.' DISCUSSION / REFLECTION / LESSONS LEARNED: In this pilot we translate discharge data from four distinct hospital systems into an educational framework to characterize our resident's educational experience and in doing so unmask disparities in exposure that could drive rational changes in rotation schedules and didactic content selection

BACKGROUND: Clinical reasoning (CR) is a core component of medical training, yet residents often receive little feedback on their CR documentation. Here we describe the process of developing a machine learning (ML) algorithm for feedback on CR documentation to increase the frequency and quality of feedback in this domain.
METHOD(S): To create this algorithm, note quality first had to be rated by gold standard human rating. We selected the IDEA Assessment Tool-a note rating instrument across four domains (I=Interpretive summary, D=Differential diagnosis, E=Explanation of reasoning, A=Alternative diagnoses explained) that uses a 3-point Likert scale without descriptive anchors. To develop descriptive anchors we conducted an iterative process reviewing notes from the EHR written by medicine residents and validated the Revised-IDEA Assessment Tool using Messick's framework- content validity, response process, relation to other variables, internal structure, and consequences. Using the Hofstee standard setting method, cutoffs for high quality clinical reasoning for the IDEA and DEA scores were set. We then created a dataset of expertrated notes to create the ML algorithm. First, a natural language processing software was applied to the set of notes that enabled recognition and automatic encoding of clinical information as a diagnosis or disease (D's), a sign or symptom (E or A), or semantic qualifier (e.g. most likely). Input variables to the ML algorithm included counts of D's, E/A's, semantic qualifiers, and proximity of semantic qualifiers to disease/ diagnosis. ML output focused on DEA quality and was binarized to low or high quality CR. Finally, 200 notes were randomly selected for human validation review comparing ML output to human rated DEA score.
RESULT(S): The IDEA and DEA scores ranged from 0-10 and 0-6, respectively. IDEA score of >= 6.5 and a DEA score of >= 3 was deemed high quality. 252 notes were rated to create the dataset and 20% were rated by 3 raters with high intraclass correlation 0.84 (95% CI 0.74-0.90). 120 of these notes comprised the testing set for ML model development. The logistic regression model was the best performing model with an AUC 0.87 and a positive predictive value (PPV) of 0.65. 48 (40%) of the notes were high quality. There was substantial interrater reliability between ML output and human rating on the 200 note validation set with a Cohen's Kappa 0.64.
CONCLUSION(S): We have developed a ML algorithm for feedback on CR documentation that we hypothesize will increase the frequency and quality of feedback in this domain. We have subsequently developed a dashboard that will display the output of the ML model. Next steps will be to provide internal medicine residents' feedback on their CR documentation using this dashboard and assess the impact this has on their documentation quality. LEARNING OBJECTIVE #1: Describe the importance of high quality documentation of clinical reasoning. LEARNING OBJECTIVE #2: Identify machine learning as a novel assessment tool for feedback on clinical reasoning documentation

Background/UNASSIGNED:Cardiovascular disease (CVD) is a growing burden in low- and middle-income countries. Ghana seeks to address this problem by task-shifting CVD diagnosis and management to nurses. The Community-Based Health Planning and Services (CHPS) initiative offers maternal and pediatric health care throughout Ghana but faces barriers to providing CVD care. We employed in-depth interviews to identify solutions to constraints in CVD care to develop a nurse-led CVD intervention in two districts of Ghana's Upper East Region. Objective/UNASSIGNED:This study sought to identify non-physician-led interventions for the screening and treatment of cardiovascular disease to incorporate into Ghana's current primary health care structure. Methods/UNASSIGNED:Using a qualitative descriptive design, we conducted 31 semistructured interviews of community health officers (CHOs) and supervising subdistrict officers (SDOs) at CHPS community facilities. Summative content analysis revealed the most common intervention ideas and endorsements by the participants. Findings/UNASSIGNED:Providers endorsed three interventions: increasing community CVD knowledge and engagement, increasing nonphysician prescribing abilities, and ensuring provider access to medical and transportation equipment. Providers suggested community leaders and volunteers should convey CVD knowledge, marshaling established gathering practices to educate communities and formulate action plans. Providers requested lectures paired with experiential learning to improve their prescribing confidence. Providers recommended revising reimbursement and equipment procurement processes for expediting access to necessary supplies. Conclusions/UNASSIGNED:Frontline CHPS primary care providers believe CVD care is feasible. They recommended a three-pronged intervention that combines community outreach, provider training, and logistical support, thereby expanding task-shifting beyond hypertension to include other CVD risk factors. This model could be replicable elsewhere.

STATEMENT OF PROBLEM OR QUESTION (ONE SENTENCE): Can user-centered design (UCD) facilitate the development of novel and effective training tools for the virtual ambulatory learning environment LEARNING OBJECTIVES 1: To identify the needs, preferences, and concerns of resident trainees and attending preceptors regarding the current virtual ambulatory care learning environment. LEARNING OBJECTIVES 2: To apply user-centered design (UCD) strategies to the development of effective tools to enhance the virtual learning experience of trainees and preceptors. DESCRIPTION OF PROGRAM/INTERVENTION, INCLUDING ORGANIZATIONAL CONTEXT (E.G. INPATIENT VS. OUTPATIENT, PRACTICE OR COMMUNITY CHARACTERISTICS): The COVID-19 pandemic spurred a rapid transition to virtual learning environments, the design of which may impact learning experiences and competency development for trainees. User-centered design (UCD) offers a framework to iteratively and collaboratively incorporate needs, preferences, and concerns of users (e.g. trainees and preceptors) in the development of acceptable and effective educational tools. This study applied UCD strategies of empathize, define, ideate, prototype, and test among Internal Medicine residents and outpatient attending preceptors to develop innovations for the virtual ambulatory care learning environment. MEASURES OF SUCCESS (DISCUSS QUALITATIVE AND/OR QUANTITATIVEMETRICSWHICHWILL BEUSEDTOEVALUATE PROGRAM/INTERVENTION): Using the UCD framework, we identified: 1) needs, preferences, and concerns of residents and preceptors in current virtual precepting practices (empathize) 2) key problem areas and pain points (define) 3) potential solutions (ideate) 4) specific products to develop (prototype), deploy, and evaluate (test) in practice FINDINGS TO DATE (IT IS NOT SUFFICIENT TO STATE FINDINGS WILL BE DISCUSSED): Qualitative needs-assessment interviews were conducted among 8 residents and 10 preceptors, which identified key areas of learner need: technical and workflow competency; the virtual precepting experience; patient rapport-building and communication; and documentation requirements. Subsequently, a Design Thinking Workshop focusing on virtual precepting was developed, and 3 workshops were conducted with 12 participants (residents and attendings). Using a three-phase interactive sequence of explore, ideate, and create, participants were divided into 2-or 3-person virtual breakout groups and asked to 1) identify a key problem in current virtual precepting, 2) brainstorm possible solutions, and 3) design and present a low-fidelity prototype of one solution. Key problems identified included: management of technical issues, goal setting for precepting sessions, clinic-specific information dissemination practices, and the loss of shared learning space with colleagues. Potential solutions included: a digital shared-learning plan for residents, a real-time virtual clinical bulletin board, an integrated virtual team huddle, and just-in-time digital chalk talks. Two prototypes are being developed for testing in the live precepting environment. KEY LESSONS FOR DISSEMINATION (WHAT CAN OTHERS TAKE AWAY FOR IMPLEMENTATION TO THEIR PRACTICE OR COMMUNITY): User-centered design can be deployed as an effective strategy to engage learners and preceptors in the design and development of educational innovations for the virtual training environment. We recommend collaborating with residents, preceptors, and other stakeholders in the iterative design of virtual learning tools

INTRODUCTION/BACKGROUND:The attitudes of healthcare staff towards patients' safety, including awareness of the risk for adverse events, are significant elements of an organization's safety culture. AIM OF RESEARCH/UNASSIGNED:To evaluate nurses and physicians' attitudes towards factors influencing hospitalized patient safety. MATERIALS AND METHODS/METHODS:The research included 606 nurses and 527 physicians employed in surgical and medical wards in 21 Polish hospitals around the country. The Polish adaptation of the Safety Attitudes Questionnaire (SAQ) was used to evaluate the factors influencing attitudes towards patient safety. RESULTS:Both nurses and physicians scored highest in stress recognition (SR) (71.6 and 80.86), while they evaluated working conditions (WC) the lowest (45.82 and 52,09). Nurses achieved statistically significantly lower scores compared to physicians in every aspect of the safety attitudes evaluation (p<0.05). The staff working in surgical wards obtained higher scores within stress recognition (SR) compared to the staff working in medical wards (78.12 vs. 73.72; p = 0.001). Overall, positive working conditions and effective teamwork can contribute to improving employees' attitudes towards patient safety. CONCLUSIONS:The results help identify unit level vulnerabilities associated with staff attitudes toward patient safety. They underscore the importance of management strategies that account for staff coping with occupational stressors to improve patient safety.

Coronavirus disease 2019 (COVID-19) was first identified at the end of 2019 as a cluster of pneumonia cases in Wuhan, China. By February 2020, this virus quickly spread, becoming a global pandemic. The spectrum of symptomatic infection severity can range from mild, severe, and critical disease. Many correlated comorbidities were established, including smoking, socioeconomic background, gender (male prevalence), hypertension, obesity, cardiovascular disease, chronic lung disease, diabetes mellitus, cancer, and chronic kidney disease. In an extensive literature search, post-COVID-19 necrotizing Staphylococcus aureus pneumonia with pneumothorax has not been recorded. We present a case about a 62-year-old male who presented with symptoms of COVID-19 with many underlying comorbidities, including hypertension and hyperlipidemia. He was on ventilatory support during his first week in the hospital and then received supplemental oxygenation as he recovered from his COVID-19 pneumonia. Nearly a month and a half after his initial presentation, he quickly decompensated and was started on supplemental oxygen and the necessary treatments. It was then, with the aid of lab work and imaging, that we determined that he had developed necrotizing Staphylococcus aureus pneumonia with pneumothorax. He was adequately treated, and once he was stable, he was discharged home and was told to continue his therapy.