Johnson, Matthew G.

Introduction/Objectives: House call programs provide care for homebound patients. We conducted a case-control study to compare hospitalization and/or emergency department (ED) use by patients enrolled in a primary care house call program versus ambulatory (community-dwelling) patients. Methods: Program enrollees (cases) were matched by age and self-reported gender to community-dwelling patients (controls). The primary exposure was the combination of hospitalization and/or ED visits for 1 year before the index date (October 1, 2024). We used unadjusted and adjusted conditional logistic regression to determine differences in health care use. Results: We identified 26 cases (median [range] age, 84 [66-98] years; 16 [62%] female) and 130 controls. All cases had 4 or more chronic diseases, as compared with 87 (67%) of controls ( P = .003). A higher proportion of cases than controls had previous hospitalization and/or ED visits (84.6% vs 27.7%; P < .001). After adjusting for the number of chronic illnesses, no differences in previous hospitalization and/or ED use were identified between groups. Conclusions: Cases had more chronic illnesses and more hospitalizations and/or ED visits than community-dwelling controls. The comorbid health burden of participants in the house call program drove greater health care use and justifies a higher level of care for this medically complex patient population.

SummaryThe goal of this study was to ascertain the effect of patient body size on clinic‐ or patient‐delayed health care during the COVID‐19 pandemic and factors predicting delay. Primary survey data were collected in August 2021 from 3593 individuals living within the communities near Mayo Clinic facilities in Minnesota, Wisconsin, Arizona, and Florida. More people who identified with the higher‐weight BMI categories reported having their care appointments cancelled (62.3%) compared to those who identified with normative weight (38.0%) or underweight (47.7%; p < .001) and reported more frequently delaying their own care (67.9%) relative to those who identified with normative weight (47.0%) or underweight (62.3%; p < .001). Higher‐weight people were more likely than others to report delay was due to not knowing where to get care (UW: 5.6%, NW: 3.5%, HW: 7.0%, pchi‐square < .001), worrying about weight‐based judgement (UW: 2.1%, NW: 2.1%, HW: 4.5%, pchi‐square < .001), and fearing discrimination (UW: 2.9%, NW: 2.4%, HW: 5.5%, pchi‐square < .001). There was no difference across groups in delay due to fear of COVID‐19 exposure. Weight gain was associated with care delay in higher‐weight and normative weight groups. Having experienced more patient‐centred care pre‐COVID was associated with less care delay in the normative weight group, but more care delay in other groups. Higher‐weight people were more likely to have their care delayed by health care providers and to delay care themselves. Patient weight gain and concerns about discrimination and judgement about weight gain may have driven the delay of needed care during COVID‐19.

Introduction: The COVID-19 pandemic has disproportionally affected historically marginalized populations and their access to resources and healthcare. In times of crisis, authentic community engagement is more important than ever. This study was Phase 1 of a larger 3-phase study to conduct timely community-engaged research with community members to understand the disproportionate impact of COVID-19 on historically underserved communities. The objective of this work was to conduct key informant (KI) interviews (1) to understand community organizations perspectives about the role that large academic health centers play as they interface with community organizations to support their work, (2) to leverage KI’s expertise to identify needs and assets within the community, and (3) to inform both Phase 2 (focus group qualitative research) and Phase 3 (survey) of the broader study. Methods: A total of 24 key informants were identified through purposeful sampling and one-on-one semi-structured interviews were conducted across 4 states using video conferencing. Results: Barriers to access and lack of transparency were highlighted as major issues requiring reform—in particular, aggressive billing practices and insurance barriers exacerbated local distrust of medical institutions. KIs recognized the health institution’s support for testing and vaccination during the COVID-19 pandemic, but noted other significant gaps in care, especially regarding mental health support. Although communication with the health institution was consistent for some KIs, others experienced unsustained communication efforts that hindered cooperation and relationship building. Conclusions: Leaders in the community as key stakeholders can provide unique insights into the challenges and potential solutions required to promote health equity, and foster understanding between local communities and healthcare institutions.

Abstract Objective We aimed to develop a model for accurate prediction of general care inpatient deterioration. Materials and Methods Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call. Results Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score. Discussion Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering. Conclusions MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

Abstract Introduction Identification of hospitalized patients with suddenly unfavorable clinical course remains challenging. Models using objective data elements from the electronic health record may miss important sources of information available to nurses. Methods We recorded nurses’ perception of patient potential for deterioration in 2 medical and 2 surgical adult hospital units using a 5-point score at the start of the shift (the Worry Factor [WF]), and any time a change or an increase was noted by the nurse. Cases were evaluated by three reviewers. Intensive care unit (ICU) transfers were also tracked. Results 31 159 patient-shifts were recorded for 3185 unique patients during 3551 hospitalizations, with 169 total outcome events. Out of 492 potential deterioration events identified, 380 (77%) were confirmed by reviewers as true deterioration events. Likelihood ratios for ICU transfer were 17.8 (15.2–20.9) in the 24 hours following a WF &gt; 2, and 40.4 (27.1–60.1) following a WF &gt; 3. Accuracy rates were significantly higher in nurses with over a year of experience (68% vs 79%, P = 0.04). The area under the receiver operator characteristic curve (AUROC) was 0.92 for the prediction of ICU transfer within 24 hours. Discussion This is a higher accuracy than most published early warning scores. Conclusion Nurses’ pattern recognition and sense of worry can provide important information for the detection of acute physiological deterioration and should be included in the electronic medical record.

BACKGROUNDThe use of rapid response systems (RRS), which were designed to bring clinicians with critical care expertise to the bedside to prevent unnecessary deaths, has increased. RRS rely on accurate detection of acute deterioration events. Early warning scores (EWS) have been used for this purpose but were developed using heterogeneous populations. Predictive performance may differ in medical vs surgical patients.OBJECTIVETo evaluate the performance of published EWS in medical vs surgical patient populations.DESIGNRetrospective cohort study.SETTINGTwo tertiary care academic medical center hospitals in the Midwest totaling more than 1500 beds.PATIENTSAll patients discharged from January to December 2011.INTERVENTIONNone.MEASUREMENTSTime‐stamped longitudinal database of patient variables and outcomes, categorized as surgical or medical. Outcomes included unscheduled transfers to the intensive care unit, activation of the RRS, and calls for cardiorespiratory resuscitation (“resuscitation call”). The EWS were calculated and updated with every new patient variable entry over time. Scores were considered accurate if they predicted an outcome in the following 24 hours.RESULTSAll EWS demonstrated higher performance within the medical population as compared to surgical: higher positive predictive value (P < .0001 for all scores) and sensitivity (P < .0001 for all scores). All EWS had positive predictive values below 25%.CONCLUSIONSThe overall poor performance of the evaluated EWS was marginally better in medical patients when compared to surgical patients.

BACKGROUNDPrior studies suggest that high workload among attending physicians may be associated with reduced teaching effectiveness and poor patient outcomes, but these relationships have not been investigated using objective measures of workload and safety.OBJECTIVETo examine associations between attending workload, teaching effectiveness, and patient safety, hypothesizing that higher workload would be associated with lower teaching effectiveness and negative patient outcomes.DESIGN, SETTING, AND PARTICIPANTSWe conducted a retrospective study of 69,386 teaching evaluation items submitted by 543 internal medicine residents for 107 attending physicians who supervised inpatient teaching services from July 2, 2005 to July 1, 2011.MEASUREMENTSAttending workload measures included hospital service census, patient length of stay, daily admissions, daily discharges, and concurrent outpatient duties. Teaching effectiveness was measured using residents' evaluations of attendings. Patient outcomes considered were applicable patient safety indicators (PSIs), intensive care unit transfers, cardiopulmonary resuscitation/rapid response team calls, and patient deaths. Mixed linear models and generalized linear regression models were used for statistical analysis.RESULTSWorkload measures of midnight census and daily discharges were associated with lower teaching evaluation scores (both β = −0.026, P < 0.0001). The number of daily admissions was associated with higher teaching scores (β = 0.021, P = 0.001) and increased PSIs (odds ratio = 1.81, P = 0.0001).CONCLUSIONSeveral measures of attending physician workload were associated with slightly lower teaching effectiveness, and patient safety may be compromised when teams are managing new admissions. Ongoing efforts by residency programs to optimize the learning environment should include strategies to manage the workload of supervising attendings. Journal of Hospital Medicine 2016;11:169–173. © 2016 Society of Hospital Medicine

ObjectiveAssess algorithms for linking patients across de‐identified databases without compromising confidentiality.Data Sources/Study SettingHospital discharges from 11 Mayo Clinic hospitals during January 2008–September 2012 (assessment and validation data). Minnesota death certificates and hospital discharges from 2009 to 2012 for entire state (application data).Study DesignCross‐sectional assessment of sensitivity and positive predictive value (PPV) for four linking algorithms tested by identifying readmissions and posthospital mortality on the assessment data with application to statewide data.Data Collection/Extraction MethodsDe‐identified claims included patient gender, birthdate, and zip code. Assessment records were matched with institutional sources containing unique identifiers and the last four digits of Social Security number (SSNL4).Principal FindingsGender, birthdate, and five‐digit zip code identified readmissions with a sensitivity of 98.0 percent and a PPV of 97.7 percent and identified postdischarge mortality with 84.4 percent sensitivity and 98.9 percent PPV. Inclusion of SSNL4 produced nearly perfect identification of readmissions and deaths. When applied statewide, regions bordering states with unavailable hospital discharge data had lower rates.ConclusionAddition of SSNL4 to administrative data, accompanied by appropriate data use and data release policies, can enable trusted repositories to link data with nearly perfect accuracy without compromising patient confidentiality. States maintaining centralized de‐identified databases should add SSNL4 to data specifications.