Care Quality for Patients with Chronic Obstructive Pulmonary Disease in the Readmission Penalty Era.

Rationale: Chronic obstructive pulmonary disease (COPD) is the fifth-leading cause of admissions and third-leading cause of readmissions among U.S. adults. Recent policies instituted financial penalties for excessive COPD readmissions. Objectives: To evaluate changes in the quality of care for patients hospitalized for COPD after implementation of the Hospital Readmissions Reduction Program (HRRP). Methods: We conducted a retrospective cohort study of patients older than 40 years of age hospitalized for COPD across 995 U.S. hospitals (Premier Healthcare Database). Measurements and Main Results: Quality of care before and after HRRP implementation was measured via adherence to recommended inpatient care treatments for acute exacerbations of COPD (recommended care, nonrecommended care, "ideal care" [all recommended and no nonrecommended care]). We included 662,842 pre-HRRP (January 2010-September 2014) and 285,508 post-HRRP (October 2014-December 2018) admissions. Recommended care increased at a rate of 0.16% per month pre-HRRP and 0.01% per month post-HRRP (P < 0.001). Nonrecommended care decreased at a rate of 0.15% per month pre-HRRP and 0.13% per month post-HRRP. Ideal care increased at a rate of 0.24% per month pre-HRRP and 0.11% per month post-HRRP (P < 0.001). Conclusions: The pre-HRRP trends toward improving care quality for inpatient COPD care slowed after HRRP implementation. This suggests that financial penalties for readmissions did not stimulate higher quality of care for patients hospitalized with COPD. It remains unclear what policies or approaches will be effective to ensure high care quality for patients hospitalized with COPD exacerbations.

No-show Prediction Model Performance Among People With HIV: External Validation Study.

BACKGROUND: Regular medical care is important for people living with HIV. A no-show predictive model among people with HIV could improve clinical care by allowing providers to proactively engage patients at high risk of missing appointments. Epic, a major provider of electronic medical record systems, created a model that predicts a patient's probability of being a no-show for an outpatient health care appointment; however, this model has not been externally validated in people with HIV. OBJECTIVE: We examined the performance of Epic's no-show model among people with HIV at an academic medical center and assessed whether the performance was impacted by the addition of demographic and HIV clinical information. METHODS: We obtained encounter data from all in-person appointments among people with HIV from January 21 to March 30, 2022, at the University of Chicago Medicine. We compared the predicted no-show probability at the time of the encounter to the actual outcome of these appointments. We also examined the performance of the Epic model among people with HIV for only HIV care appointments in the infectious diseases department. We further compared the no-show model among people with HIV for HIV care appointments to an alternate random forest model we created using a subset of seven readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. RESULTS: We identified 674 people with HIV who contributed 1406 total scheduled in-person appointments during the study period. Of those, we identified 331 people with HIV who contributed 440 HIV care appointments. The performance of the Epic model among people with HIV for all appointments in any outpatient clinic had an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI 0.63-0.66) and for only HIV care appointments had an AUC of 0.63 (95% CI 0.59-0.67). The alternate model we created for people with HIV attending HIV care appointments had an AUC of 0.78 (95% CI 0.75-0.82), a significant improvement over the Epic model restricted to HIV care appointments (P<.001). Features identified as important in the alternate model included lead time, appointment length, HIV viral load >200 copies per mL, lower CD4 T cell counts (both 50 to <200 cells/mm3 and 200 to <350 cells/mm3), and female sex. CONCLUSIONS: For both models among people with HIV, performance was significantly lower than reported by Epic. The improvement in the performance of the alternate model over the proprietary Epic model demonstrates that, among people with HIV, the inclusion of demographic information may enhance the prediction of appointment attendance. The alternate model further reveals that the prediction of appointment attendance in people with HIV can be improved by using HIV clinical information such as CD4 count and HIV viral load test results as features in the model.

Application of matrix-assisted laser desorption/ionization mass spectrometry to identify species of Neotropical Anopheles vectors of malaria.

BACKGROUND: Malaria control in Panama is problematic due to the high diversity of morphologically similar Anopheles mosquito species, which makes identification of vectors of human Plasmodium challenging. Strategies by Panamanian health authorities to bring malaria under control targeting Anopheles vectors could be ineffective if they tackle a misidentified species. METHODS: A rapid mass spectrometry identification procedure was developed to accurately and timely sort out field-collected Neotropical Anopheles mosquitoes into vector and non-vector species. Matrix-assisted laser desorption/ionization (MALDI) mass spectra of highly-abundant proteins were generated from laboratory-reared mosquitoes using different extraction protocols, body parts, and sexes to minimize the amount of material from specimen vouchers needed and optimize the protocol for taxonomic identification. Subsequently, the mass spectra of field-collected Neotropical Anopheles mosquito species were classified using a combination of custom-made unsupervised (i.e., Principal component analysis-PCA) and supervised (i.e., Linear discriminant analysis-LDA) classification algorithms. RESULTS: Regardless of the protocol used or the mosquito species and sex, the legs contained the least intra-specific variability with enough well-preserved proteins to differentiate among distinct biological species, consistent with previous literature. After minimizing the amount of material needed from the voucher, one leg was enough to produce reliable spectra between specimens. Further, both PCA and LDA were able to classify up to 12 mosquito species, from different subgenera and seven geographically spread localities across Panama using mass spectra from one leg pair. LDA demonstrated high discriminatory power and consistency, with validation and cross-validation positive identification rates above 93% at the species level. CONCLUSION: The selected sample processing procedure can be used to identify field-collected Anopheles species, including vectors of Plasmodium, in a short period of time, with a minimal amount of tissue and without the need of an expert mosquito taxonomist. This strategy to analyse protein spectra overcomes the drawbacks of working without a reference library to classify unknown samples. Finally, this MALDI approach can aid ongoing malaria eradication efforts in Panama and other countries with large number of mosquito's species by improving vector surveillance in epidemic-prone sites such as indigenous Comarcas.

Quantifying the Effects of Prior Acetyl-Salicylic Acid on Sepsis-Related Deaths: An Individual Patient Data Meta-Analysis Using Propensity Matching.

OBJECTIVE: The primary objective was to conduct a meta-analysis on published observational cohort data describing the association between acetyl-salicylic acid (aspirin) use prior to the onset of sepsis and mortality in hospitalized patients. STUDY SELECTION: Studies that reported mortality in patients on aspirin with sepsis with a comparison group of patients with sepsis not on prior aspirin therapy were included. DATA SOURCES: Fifteen studies described hospital-based cohorts (n = 17,065), whereas one was a large insurance-based database (n = 683,421). Individual-level patient data were incorporated from all selected studies. DATA EXTRACTION: Propensity analyses with 1:1 propensity score matching at the study level were performed, using the most consistently available covariates judged to be associated with aspirin. Meta-analyses were performed to estimate the pooled average treatment effect of aspirin on sepsis-related mortality. DATA SYNTHESIS: Use of aspirin was associated with a 7% (95% CI, 2-12%; p = 0.005) reduction in the risk of death as shown by meta-analysis with considerable statistical heterogeneity (I = 61.6%). CONCLUSIONS: These results are consistent with effects ranging from a 2% to 12% reduction in mortality risk in patients taking aspirin prior to sepsis onset. This association anticipates results of definitive studies of the use of low-dose aspirin as a strategy for reduction of deaths in patients with sepsis.

Validating the Physician Documentation Quality Instrument for Intensive Care Unit-Ward Transfer Notes.

Background: Physician communication failures during transfers of patients from the intensive care unit (ICU) to the general ward are common and can lead to adverse events. Efforts to improve written handoffs during these transfers are increasingly prominent, but no instruments have been developed to assess the quality of physician ICU-ward transfer notes. Objective: To collect validity evidence for the modified nine-item Physician Documentation Quality Instrument (mPDQI-9) for assessing ICU-ward transfer note usefulness across several hospitals. Methods: Twenty-four physician raters independently used the mPDQI-9 to grade 12 notes collected from three academic hospitals. A priori, we excluded the "up-to-date" and "accurate" domains, because these could not be assessed without giving the rater access to the complete patient chart. Assessments therefore used the domains "thorough," "useful," "organized," "comprehensible," "succinct," "synthesized," and "consistent." Raters scored each domain on a Likert scale ranging from 1 (low) to 5 (high). The total mPDQI-9 was the sum of these domain scores. The primary outcome was the raters' perceived clinical utility of the notes, and the primary measures of interest were criterion validity (Spearman's ρ) and interrater reliability (intraclass correlation [ICC]). Results: Mean mPDQI-9 scores by note ranged from 19 (SD = 5.5) to 30 (SD = 4.2). Mean note ratings did not systematically differ by rater expertise (for interaction, P = 0.15). The proportion of raters perceiving each note as independently sufficient for patient care (the primary outcome) ranged from 33% to 100% across the set of notes. We found a moderately positive correlation between mPDQI-9 ratings and raters' overall assessments of each note's clinical utility (ρ = 0.48, P < 0.001). Interrater reliability was strong; the overall ICC was 0.89 (95% confidence interval [CI], 0.80-0.85), and ICCs were similar among reviewer groups. Finally, Cronbach's α was 0.87 (95% CI, 0.84-0.89), indicating good internal consistency. Conclusions: We report moderate validity evidence for the mPDQI-9 to assess the usefulness of ICU-ward transfer notes written by internal medicine residents.

Evaluating Quality of Care for Patients with Asthma in the Readmission Penalty Era.

RATIONALE: Asthma poses a significant burden for US patients and health systems, yet inpatient care quality is understudied. National chronic obstructive lung disease (COPD) readmission policies may affect inpatient asthma care through hospital responses to these polices due to imprecise diagnosis and identification of patients with COPD and asthma. OBJECTIVES: Evaluate inpatient care quality care for patients hospitalized with asthma and potential collateral effects of the Medicare COPD Hospital Readmissions Reduction Program (HRRP). METHODS: Retrospective cohort study of patients aged 18-54 years hospitalized for asthma across 924 US hospitals (Premier Healthcare Database). RESULTS: Care quality for patients with asthma was evaluated pre-HRRP implementation (n=20,820; January 2010-September 2014) and post-HRRP implementation (n=26,885; October 2014-December 2018) using adherence to inpatient care guidelines (recommended, non-recommended, and "ideal care" [all recommended with no non-recommended care]). Between 2010-2018, at least 80% of patients received recommended care annually. Recommended care decreased similarly (rate of 0.02%/month) post vs. pre-HRRP (p=0.8). Non-recommended care decreased more rapidly post-HRRP (rate of 0.29%/month) vs. pre-HRRP (rate of 0.17%/month; p<0.001) with changes driven largely by decreased antibiotic prescribing. Ideal care increased more rapidly post-HRRP (rate of 0.25%/month) vs. pre-HRRP (rate of 0.17%/month; p=0.02) with changes driven largely by non-recommended care improvements. CONCLUSIONS: Post-HRRP trends suggest asthma care improved with increased rates of guideline concordance in non-recommended and ideal care. While federal policies (e.g., HRRP) may have had positive collateral effects such as with asthma care, parallel care efforts including antibiotic stewardship likely contributed to these improvements.

Association between Dexmedetomidine Use and Mortality in Patients with COVID-19 Receiving Invasive Mechanical Ventilation: A U.S. National COVID Cohort Collaborative (N3C) Study.

(1) Background/Objectives: Dexmedetomidine is a sedative for patients receiving invasive mechanical ventilation (IMV) that previous single-site studies have found to be associated with improved survival in patients with COVID-19. The reported clinical benefits include dampened inflammatory response, reduced respiratory depression, reduced agitation and delirium, improved preservation of responsiveness and arousability, and improved hypoxic pulmonary vasoconstriction and ventilation-perfusion ratio. Whether improved mortality is evident in large, multi-site COVID-19 data is understudied. (2) Methods: The association between dexmedetomidine use and mortality in patients with COVID-19 receiving IMV was assessed. This retrospective multi-center cohort study utilized patient data in the United States from health systems participating in the National COVID Cohort Collaborative (N3C) from 1 January 2020 to 3 November 2022. The primary outcome was 28-day mortality rate from the initiation of IMV. Propensity score matching adjusted for differences between the group with and without dexmedetomidine use. Adjusted hazard ratios (aHRs) for 28-day mortality were calculated using multivariable Cox proportional hazards models with dexmedetomidine use as a time-varying covariate. (3) Results: Among the 16,357,749 patients screened, 3806 patients across 17 health systems met the study criteria. Mortality was lower with dexmedetomidine use (aHR, 0.81; 95% CI, 0.73-0.90; p < 0.001). On subgroup analysis, mortality was lower with earlier dexmedetomidine use-initiated within the median of 3.5 days from the start of IMV-(aHR, 0.67; 95% CI, 0.60-0.76; p < 0.001) as well as use prior to standard, widespread use of dexamethasone for patients on respiratory support (prior to 30 July 2020) (aHR, 0.54; 95% CI, 0.42-0.69; p < 0.001). In a secondary model that was restricted to 576 patients across six health system sites with available PaO2/FiO2 data, mortality was not lower with dexmedetomidine use (aHR 0.95, 95% CI, 0.72-1.25; p = 0.73); however, on subgroup analysis, mortality was lower with dexmedetomidine use initiated earlier than the median dexmedetomidine start time after IMV (aHR, 0.72; 95% CI, 0.53-0.98; p = 0.04) and use prior to 30 July 2020 (aHR, 0.22; 95% CI, 0.06-0.78; p = 0.02). (4) Conclusions: Dexmedetomidine use was associated with reduced mortality in patients with COVID-19 receiving IMV, particularly when initiated earlier, rather than later, during the course of IMV as well as use prior to the standard, widespread usage of dexamethasone during respiratory support. These particular findings might suggest that the associated mortality benefit with dexmedetomidine use is tied to immunomodulation. However, further research including a large randomized controlled trial is warranted to evaluate the potential mortality benefit of DEX use in COVID-19 and evaluate the physiologic changes influenced by DEX that may enhance survival.

Association of preadmission insomnia symptoms with objective in-hospital sleep and clinical outcomes among hospitalized patients.

STUDY OBJECTIVES: To determine the prevalence of preadmission insomnia symptoms among hospitalized patients and assess the association of insomnia symptoms with objective in-hospital sleep and clinical outcomes. METHODS: We conducted a prospective cohort study of medicine inpatients (age ≥ 50, no previously diagnosed sleep disorders). Participants answered the Insomnia Severity Index (ISI) questionnaire to assess for preadmission insomnia symptoms (scored 0-28; higher scores suggest more insomnia symptoms). Sleep duration and efficiency were measured with actigraphy. Participants self-reported 30-day postdischarge readmissions and emergency department and/or urgent care visits. RESULTS: Of 568 participants, 49% had ISI scores suggestive of possible undiagnosed insomnia (ISI ≥ 8). Higher ISI scores were associated with shorter sleep duration [ß = -2.6, 95% confidence interval (CI) -4.1 to -1.1, P = .001] and lower sleep efficiency (ß = -0.39, 95% CI -0.63 to -0.15, P = .001). When adjusted for age, sex, body mass index, and comorbidities, higher ISI scores were associated with longer length of stay (incidence rate ratio 1.01, 95% CI 1.00-1.02, P = .011), increased risk of 30-day readmission (odds ratio 1.04, 95% CI 1.01-1.07, P = .018), and increased risk of 30-day emergency department or urgent care visit (odds ratio 1.04, 95% CI 1.00-1.07, P = .043). CONCLUSIONS: Among medicine inpatients, there was a high prevalence of preadmission insomnia symptoms suggestive of possible undiagnosed insomnia. Participants with higher ISI scores slept less with lower sleep efficiency during hospitalization. Higher ISI scores were associated with longer length of stay, increased risk of a 30-day postdischarge readmission, and increased risk of a 30-day postdischarge emergency department or urgent care visit. CITATION: Neborak JM, Press VG, Parker WF, et al. Association of preadmission insomnia symptoms with objective in-hospital sleep and clinical outcomes among hospitalized patients. J Clin Sleep Med. 2024;20(5):681-687.

Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated.

Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers exist to facilitating trust between clinicians and AI-based tools, limiting its current impact. Potential solutions are available at both the local and national level. It will take a broad and diverse coalition of stakeholders, from health-care systems, EHR vendors, and clinical educators to regulators, researchers and the patient community, to help facilitate this trust so that the promise of AI in health care can be realized.

Improving Communication in Intensive Care Unit to Ward Transitions: Protocol for Multisite National Implementation of the ICU-PAUSE Handoff Tool.

BACKGROUND: The intensive care unit (ICU)-ward transfer poses a particularly high-risk period for patients. The period after transfer has been associated with adverse events and additional work for care teams related to miscommunication or omission of information. Standardized handoff processes have been found to reduce communication errors and adverse patient events in other clinical environments but are understudied at the ICU-ward interface. We previously developed an electronic ICU-ward transfer tool, ICU-PAUSE, which embeds the key elements and diagnostic reasoning to facilitate a safe transfer of care at ICU discharge. OBJECTIVE: The aim of this study is to evaluate the implementation process of the ICU-PAUSE handoff tool across 10 academic medical centers, including the rate of adoption and acceptability, as perceived by clinical care teams. METHODS: ICU-PAUSE will be implemented in the medical ICU across 10 academic hospitals, with each site customizing the tool to their institution's needs. Our mixed methods study will include a combination of a chart review, quantitative surveys, and qualitative interviews. After a 90-day implementation period, we will conduct a retrospective chart review to evaluate the rate of uptake of ICU-PAUSE. We will also conduct postimplementation surveys of providers to assess perceptions of the tool and its impact on the frequency of communication errors and adverse events during ICU-ward transfers. Lastly, we will conduct semistructured interviews of faculty stakeholders with subsequent thematic analysis with the goal of identifying benefits and barriers in implementing and using ICU-PAUSE. RESULTS: ICU-PAUSE was piloted in the medical ICU at Barnes-Jewish Hospital, the teaching hospital of Washington University School of Medicine in St. Louis, in 2019. As of July 2022, implementation of ICU-PAUSE is ongoing at 6 of 10 participating sites. Our results will be published in 2023. CONCLUSIONS: Our process of ICU-PAUSE implementation embeds each step of template design, uptake, and customization in the needs of users and key stakeholders. Here, we introduce our approach to evaluate its acceptability, usability, and impact on communication errors according to the tenets of sociotechnical theory. We anticipate that ICU-PAUSE will offer an effective handoff tool for the ICU-ward transition that can be generalized to other institutions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40918.

Scaling up a diagnostic pause at the ICU-to-ward transition: an exploration of barriers and facilitators to implementation of the ICU-PAUSE handoff tool.

OBJECTIVES: The transition from the intensive care unit (ICU) to the medical ward is a high-risk period due to medical complexity, reduced patient monitoring, and diagnostic uncertainty. Standardized handoff practices reduce errors associated with transitions of care, but little work has been done to standardize the ICU to ward handoff. Further, tools that exist do not focus on preventing diagnostic error. Using Human-Centered Design methods we previously created a novel EHR-based ICU-ward handoff tool (ICU-PAUSE) that embeds a diagnostic pause at the time of transfer. This study aims to explore barriers and facilitators to implementing a diagnostic pause at the ICU-to-ward transition. METHODS: This is a multi-center qualitative study of semi-structured interviews with intensivists from ten academic medical centers. Interviews were analyzed iteratively through a grounded theory approach. The Sittig-Singh sociotechnical model was used as a unifying conceptual framework. RESULTS: Across the eight domains of the model, we identified major benefits and barriers to implementation. The embedded pause to address diagnostic uncertainty was recognized as a key benefit. Participants agreed that standardization of verbal and written handoff would decrease variation in communication. The main barriers fell within the domains of workflow, institutional culture, people, and assessment. CONCLUSIONS: This study represents a novel application of the Sittig-Singh model in the assessment of a handoff tool. A unique feature of ICU-PAUSE is the explicit acknowledgement of diagnostic uncertainty, a practice that has been shown to reduce medical error and prevent premature closure. Results will be used to inform future multi-site implementation efforts.

Cocreating the ICU-PAUSE Tool for Intensive Care Unit-Ward Transitions.

Background: Intensive care unit (ICU)-ward patient transfers are inherently high risk, and clinician miscommunication has been linked to adverse events and negative outcomes. Despite these risks, few educational tools exist to improve resident handoff communication at ICU-ward transfer. Objective: We used human-centered design (HCD) methods to cocreate a novel electronic health record ICU-ward transfer tool alongside Internal Medicine residents at three academic hospitals. Methods: We conducted HCD workshops at each hospital, performing process mapping, brainstorming, and rapid prototyping. We performed thematic analysis on verbatim-transcribed workshop audio recordings to inform development and adaptation of the final resident prototype into the ICU-PAUSE tool. Results: ICU-PAUSE focuses on reasons for ICU admission and problem-based ICU course (I); Code status, goals of care, and family contacts (C); a diagnostic pause acknowledging Uncertainty (U); Pending tests (P); Active consultants (A); high-risk medications, including medications to be Unprescribed (U); Summary of problems and to-dos (S); and a current physical Exam (E). Conclusion: We used HCD to cocreate a novel, more user-friendly electronic ICU-ward transfer tool, ICU-PAUSE, alongside Internal Medicine trainees. Future steps will involve formal usability testing, evidence-driven implementation, and clinical evaluation of ICU-PAUSE across multiple hospitals.

Framework for Integrating Equity Into Machine Learning Models: A Case Study.

Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical outcomes and the efficiency of care delivery for all patients. Unfortunately, predictive models could harm populations that have experienced interpersonal, institutional, and structural biases. Models learn from historically collected data that could be biased. In addition, bias impacts a model's development, application, and interpretation. We present a strategy to evaluate for and mitigate biases in machine learning models that potentially could create harm. We recommend analyzing for disparities between less and more socially advantaged populations across model performance metrics (eg, accuracy, positive predictive value), patient outcomes, and resource allocation and then identify root causes of the disparities (eg, biased data, interpretation) and brainstorm solutions to address the disparities. This strategy follows the lifecycle of machine learning models in health care, namely, identifying the clinical problem, model design, data collection, model training, model validation, model deployment, and monitoring after deployment. To illustrate this approach, we use a hypothetical case of a health system developing and deploying a machine learning model to predict the risk of mortality in 6 months for patients admitted to the hospital to target a hospital's delivery of palliative care services to those with the highest mortality risk. The core ethical concepts of equity and transparency guide our proposed framework to help ensure the safe and effective use of predictive algorithms in health care to help everyone achieve their best possible health.

Integrating Physicians Into Lean Quality Improvement Through a Structured Educational Program: The ECLIPSE Program.

In the increasingly complex health care system, physicians require skills and knowledge to participate with multidisciplinary team members in quality improvement (QI) that adds value to health care organizations. The Educational and Clinical Leaders Improving Performance with Structured E3L training (ECLIPSE) program was developed to address this challenge. Clinically relevant components of lean management were leveraged to create an online, flipped-classroom curriculum, and this was paired with Kaizen adapted specifically for physicians and multidisciplinary clinicians to promote experiential skills utilization. The focus of each adapted Kaizen was a topic of institutional QI priority, such as improving patient throughput or reducing readmission rates. Participants were awarded certification in the E3 Leadership management system-a patient-centered, equity-focused system based on lean principles. After 4 years, 50 E3 Leadership certificates were awarded to multidisciplinary clinicians, including 30 to physicians; participants scored an average 85% on module quizzes. The ECLIPSE program has improved physician participation in multidisciplinary QI projects with institutional alignment.

The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design.

BACKGROUND: Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. OBJECTIVE: This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use. METHODS: A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models. RESULTS: The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025. CONCLUSIONS: The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42971.

Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation.

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital readmissions. Few existing tools use electronic health record (EHR) data to forecast patients' readmission risk during index hospitalizations. Objective: We used machine learning and in-hospital data to model 90-day risk for and cause of readmission among inpatients with acute exacerbations of COPD (AE-COPD). Design: Retrospective cohort study. Participants: Adult patients admitted for AE-COPD at the University of Chicago Medicine between November 7, 2008 and December 31, 2018 meeting International Classification of Diseases (ICD)-9 or -10 criteria consistent with AE-COPD were included. Methods: Random forest models were fit to predict readmission risk and respiratory-related readmission cause. Predictor variables included demographics, comorbidities, and EHR data from patients' index hospital stays. Models were derived on 70% of observations and validated on a 30% holdout set. Performance of the readmission risk model was compared to that of the HOSPITAL score. Results: Among 3238 patients admitted for AE-COPD, 1103 patients were readmitted within 90 days. Of the readmission causes, 61% (n = 672) were respiratory-related and COPD (n = 452) was the most common. Our readmission risk model had a significantly higher area under the receiver operating characteristic curve (AUROC) (0.69 [0.66, 0.73]) compared to the HOSPITAL score (0.63 [0.59, 0.67]; p = 0.002). The respiratory-related readmission cause model had an AUROC of 0.73 [0.68, 0.79]. Conclusion: Our models improve on current tools by predicting 90-day readmission risk and cause at the time of discharge from index admissions for AE-COPD. These models could be used to identify patients at higher risk of readmission and direct tailored post-discharge transition of care interventions that lower readmission risk.

Analysis and comparison of image processing and artificial intelligence algorithms to detect AFB in pulmonary tuberculosis images.

Pulmonary tuberculosis (TB) is one of the top 10 causes of death worldwide caused by an infection. TB is curable with an adequate diagnosis, normally performed through bacilloscopies. Automate TB diagnosis implies bacilli detection and counting usually based on smear images processing and artificial intelligence. Works reported in the literature usually consider images with similar coloring characteristics, which are difficult to obtain due to the Ziehl - Neelsen staining method variations (excess or deficiency of coloration), provoking errors in the bacilli segmentation. This paper presents an image preprocessing technique, based on simple, fast and well-known processing techniques, to improve and standardize the contrast in the Acid-Fast Bacilli (AFB) images used to diagnose TB; these techniques are used previously to the segmentation stage to obtain accurate results. The results are validated with and without the preprocessing stage by the Jaccard index, pixel detection accuracy and UAC obtained in an Artificial Neural Network (ANN) and a Bayesian classifier with Gaussian mixture model (GMM). Obtained results indicate that the proposed approach can be applied to automate the Tuberculosis diagnostic.