Novel Single-cell Measurements Suggest Irreversibly Sickled Cells Are Neither Dense Nor Dehydrated.

In sickle cell anemia, deoxygenation causes erythrocytes to distort, while reoxgenation permits them to recover a normal biconcave disk shape. Irreversibly sickled cells (ISCs) cells remain distorted when reoxygenated and have been thought to have among the highest intracellular hemoglobin concentration of the sickle red cell population and therefore the greatest vulnerability to vaso-occlusion. Using a new optical method, which we describe, we have made precise measurements of the intracellular hemoglobin concentration, and intracellular O2 saturation, of ISCs, as well as oxygenated sickle cells with a normal biconcave disc shape, and cells with shapes distorted by the sickle fibers they contain. This method also provides good estimates of cell volumes, and hemoglobin per red cell. The concentration distribution of the ISCs is found to be similar to normal, discoid cells. Average ISC volumes exceed their discoid counterparts, with a much broader distribution, arguing against dehydration as their origin. The concentration distribution of the polymer-laden sickled cells is significantly higher in mean value, and their volume distributions indicate some dehydration. Previous assumptions about ISCs may have thus been colored by the presence of sickle cells that did contain polymer, and that true ISC's may be much more benign than once thought, which underscores the importance of accurate measurement on individual cells. This method could be used to follow changes in individual cell properties under various specific perturbations, and where characterization by flow cytometry is infeasible.

Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration.

BACKGROUND: Early detection of clinical deterioration among hospitalized patients is a clinical priority for patient safety and quality of care. Current automated approaches for identifying these patients perform poorly at identifying imminent events. OBJECTIVE: Develop a machine learning algorithm using pager messages sent between clinical team members to predict imminent clinical deterioration. DESIGN: We conducted a large observational study using long short-term memory machine learning models on the content and frequency of clinical pages. PARTICIPANTS: We included all hospitalizations between January 1, 2018 and December 31, 2020 at Vanderbilt University Medical Center that included at least one page message to physicians. Exclusion criteria included patients receiving palliative care, hospitalizations with a planned intensive care stay, and hospitalizations in the top 2% longest length of stay. MAIN MEASURES: Model classification performance to identify in-hospital cardiac arrest, transfer to intensive care, or Rapid Response activation in the next 3-, 6-, and 12-hours. We compared model performance against three common early warning scores: Modified Early Warning Score, National Early Warning Score, and the Epic Deterioration Index. KEY RESULTS: There were 87,783 patients (mean [SD] age 54.0 [18.8] years; 45,835 [52.2%] women) who experienced 136,778 hospitalizations. 6214 hospitalized patients experienced a deterioration event. The machine learning model accurately identified 62% of deterioration events within 3-hours prior to the event and 47% of events within 12-hours. Across each time horizon, the model surpassed performance of the best early warning score including area under the receiver operating characteristic curve at 6-hours (0.856 vs. 0.781), sensitivity at 6-hours (0.590 vs. 0.505), specificity at 6-hours (0.900 vs. 0.878), and F-score at 6-hours (0.291 vs. 0.220). CONCLUSIONS: Machine learning applied to the content and frequency of clinical pages improves prediction of imminent deterioration. Using clinical pages to monitor patient acuity supports improved detection of imminent deterioration without requiring changes to clinical workflow or nursing documentation.

Impact of inpatient addiction psychiatry consultation on opioid use disorder outcomes.

BACKGROUND AND OBJECTIVES: Addiction consultation services provide access to specialty addiction care during general hospital admission. This study assessed opioid use disorder (OUD) outcomes associated with addiction consultation. METHODS: Retrospective cohort study of individuals with OUD admitted to an academic medical center between 2018 and 2023. The exposure was addiction consultation. Outcomes included initiating medication for OUD (MOUD), hospital length of stay, before-medically-advised (BMA) discharge, and 30- and 90-day postdischarge acute care utilization. RESULTS: Of 26,766 admissions (10,501 patients) with OUD, 2826 addiction consultations were completed. Consultation cohort was more likely to be young, male, and White than controls. Consultation was associated with greater MOUD initiation (adjusted odds ratio [aOR], 5.07; 95% confidence interval [CI], 4.41-5.82), fewer emergency department visits at 30 (aOR, 0.78; 95% CI, 0.67-0.92) and 90 (aOR, 0.79; 95% CI, 0.69-0.89) days, and fewer hospitalizations at 30 (aOR, 0.65; 95% CI, 0.56 to 0.76) and 90 (aOR, 0.67; 95% CI, 0.59-0.76) days. Additionally, consultation patients were more likely to have a longer hospital stay and leave BMA. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Addiction consultation was associated with increased MOUD initiation and reduced postdischarge acute care utilization. This is the largest study to date showing a significant association between addiction psychiatry consultation and improved OUD outcomes when compared to controls. The observed reduction in postdischarge acute care utilization remains even after adjusting for MOUD initiation. Disparities in access to addiction consultation warrant further study.

Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation.

OBJECTIVE: Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS: The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS: The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION: Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION: As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations.

A systematic review of theoretical constructs in CDS literature.

BACKGROUND: Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature. OBJECTIVE: Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions. METHODS: We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters. RESULTS: Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87). CONCLUSION: We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.

Iterative heuristic design of temporal graphic displays with clinical domain experts.

Conventional electronic health record information displays are not optimized for efficient information processing. Graphical displays that integrate patient information can improve information processing, especially in data-rich environments such as critical care. We propose an adaptable and reusable approach to patient information display with modular graphical components (widgets). We had two study objectives. First, reduce numerous widget prototype alternatives to preferred designs. Second, derive widget design feature recommendations. Using iterative human-centered design methods, we interviewed experts to hone design features of widgets displaying frequently measured data elements, e.g., heart rate, for acute care patient monitoring and real-time clinical decision-making. Participant responses to design queries were coded to calculate feature-set agreement, average prototype score, and prototype agreement. Two iterative interview cycles covering 64 design queries and 86 prototypes were needed to reach consensus on six feature sets. Interviewers agreed that line graphs with a smoothed or averaged trendline, 24-h timeframe, and gradient coloring for urgency were useful and informative features. Moreover, users agreed that widgets should include key functions: (1) adjustable reference ranges, (2) expandable timeframes, and (3) access to details on demand. Participants stated graphical widgets would be used to identify correlating patterns and compare abnormal measures across related data elements at a specific time. Combining theoretical principles and validated design methods was an effective and reproducible approach to designing widgets for healthcare displays. The findings suggest our widget design features and recommendations match critical care clinician expectations for graphical information display of continuous and frequently updated patient data.

Medical and genetic correlates of long-term buprenorphine treatment in the electronic health records.

Despite the benefits associated with longer buprenorphine treatment duration (i.e., >180 days) (BTD) for opioid use disorder (OUD), retention remains poor. Research on the impact of co-occurring psychiatric issues on BTD has yielded mixed results. It is also unknown whether the genetic risk in the form of polygenic scores (PGS) for OUD and other comorbid conditions, including problematic alcohol use (PAU) are associated with BTD. We tested the association between somatic and psychiatric comorbidities and long BTD and determined whether PGS for OUD-related conditions was associated with BTD. The study included 6686 individuals with a buprenorphine prescription that lasted for less than 6 months (short-BTD) and 1282 individuals with a buprenorphine prescription that lasted for at least 6 months (long-BTD). Recorded diagnosis of substance addiction and disorders (Odds Ratio (95% CI) = 22.14 (21.88-22.41), P = 2.8 × 10-116), tobacco use disorder (OR (95% CI) = 23.4 (23.13-23.68), P = 4.5 × 10-111), and bipolar disorder (OR(95% CI) = 9.70 (9.48-9.92), P = 1.3 × 10-91), among others, were associated with longer BTD. The PGS of OUD and several OUD co-morbid conditions were associated with any buprenorphine prescription. A higher PGS for OUD (OR per SD increase in PGS (95%CI) = 1.43(1.16-1.77), P = 0.0009), loneliness (OR(95% CI) = 1.39(1.13-1.72), P = 0.002), problematic alcohol use (OR(95%CI) = 1.47(1.19-1.83), P = 0.0004), and externalizing disorders (OR(95%CI) = 1.52(1.23 to 1.89), P = 0.0001) was significantly associated with long-BTD. Associations between BTD and the PGS of depression, chronic pain, nicotine dependence, cannabis use disorder, and bipolar disorder did not survive correction for multiple testing. Longer BTD is associated with diagnoses of psychiatric and somatic conditions in the EHR, as is the genetic score for OUD, loneliness, problematic alcohol use, and externalizing disorders.

Provider Perceptions Toward Extended-Release Buprenorphine for Treatment of Opioid Use Disorder.

OBJECTIVES: The persistence of the opioid crisis and the proliferation of synthetic fentanyl have heightened the demand for the implementation of novel delivery mechanisms of pharmacotherapy for the treatment of opioid use disorder, including injectable extended-release buprenorphine (buprenorphine-ER). The purpose of this study was to understand provider-level barriers to prescribing buprenorphine in order to facilitate targeted strategies to improve implementation for patients who would benefit from this novel formulation. METHODS: Using an interview template adapted from the Consolidated Framework for Implementation Research (CFIR), we conducted structured focus group interviews with 20 providers in an outpatient addiction clinic across 4 sessions to assess providers' perceptions of buprenorphine-ER. Ninety-four unique comments were identified and deductively coded using standardized CFIR constructs. RESULTS: Providers expressed mixed receptivity and confidence in using buprenorphine-ER. Although providers could identify a number of theoretical advantages to the injectable formulation over sublingual buprenorphine, many expressed reservations about using it due to inexperience, negative patient experiences, uncertainties about patient candidacy, cost, and logistical constraints. CONCLUSIONS: Provider concerns about buprenorphine-ER may limit utilization. Some concerns may be mitigated through improved education, research, and logistical support. Given the putative benefits of buprenorphine-ER, future research should target barriers to implementation, in part based on hypotheses generated by these findings.