Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

Importance: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioration, but there is limited evidence for their effectiveness. Objective: To evaluate the effectiveness of an artificial intelligence deterioration model-enabled intervention to reduce the risk of escalations in care among hospitalized patients using a study design that facilitates stronger causal inference. Design, Setting, and Participants: This cohort study used a regression discontinuity design that controlled for confounding and was based on Epic Deterioration Index (EDI; Epic Systems Corporation) prediction model scores. Compared with other observational research, the regression discontinuity design facilitates causal analysis. Hospitalized adults were included from 4 general internal medicine units in 1 academic hospital from January 17, 2021, through November 16, 2022. Exposure: An artificial intelligence deterioration model-enabled intervention, consisting of alerts based on an EDI score threshold with an associated collaborative workflow among nurses and physicians. Main Outcomes and Measures: The primary outcome was escalations in care, including rapid response team activation, transfer to the intensive care unit, or cardiopulmonary arrest during hospitalization. Results: During the study, 9938 patients were admitted to 1 of the 4 units, with 963 patients (median [IQR] age, 76.1 [64.2-86.2] years; 498 males [52.3%]) included within the primary regression discontinuity analysis. The median (IQR) Elixhauser Comorbidity Index score in the primary analysis cohort was 10 (0-24). The intervention was associated with a -10.4-percentage point (95% CI, -20.1 to -0.8 percentage points; P = .03) absolute risk reduction in the primary outcome for patients at the EDI score threshold. There was no evidence of a discontinuity in measured confounders at the EDI score threshold. Conclusions and Relevance: Using a regression discontinuity design, this cohort study found that the implementation of an artificial intelligence deterioration model-enabled intervention was associated with a significantly decreased risk of escalations in care among inpatients. These results provide evidence for the effectiveness of this intervention and support its further expansion and testing in other care settings.

Quality improvement project to reduce medicare 1-day write-offs due to inappropriate admission orders.

BACKGROUND: We identified that Stanford Health Care had a significant number of patients who after discharge are found by the utilization review committee not to meet Center for Mediare and Medicaid Services (CMS) 2-midnight benchmark for inpatient status. Some of the charges incurred during the care of these patients are written-off and known as Medicare 1-day write-offs. This study which aims to evaluate the use of a Best Practice Alert (BPA) feature on the electronic medical record, EPIC, to ensure appropriate designation of a patient's hospitalization status as either inpatient or outpatient in accordance with Center for Medicare and Medicaid services (CMS) 2 midnight length of stay benchmark thereby reducing the number of associated write-offs. METHOD: We incorporated a best practice alert (BPA) into the Epic Electronic Medical Record (EMR) that would prompt the discharging provider and the case manager to review the patients' inpatient designation prior to discharge and change the patient's designation to observation when deemed appropriate. Patients who met the inclusion criteria (Patients must have Medicare fee-for-service insurance, inpatient length of stay (LOS) less than 2 midnights, inpatient designation as hospitalization status at time of discharge, was hospitalized to an acute level of care and belonged to one of 37 listed hospital services at the time of signing of the discharge order) were randomized to have the BPA either silent or active over a three-month period from July 18, 2019, to October 18, 2019. RESULT: A total of 88 patients were included in this study: 40 in the control arm and 48 in the intervention arm. In the intervention arm, 8 (8/48, 16.7%) had an inpatient status designation despite potentially meeting Medicare guidelines for an observation stay, comparing to 23 patients (23/40, 57.5%) patients in the control group (p = 0.001). The estimated number of write-offs in the control arm was 17 (73.9%, out of 23 inpatient patients) while in the intervention arm was 1 (12.5%, out of 8 inpatient patient) after accounting for patients who may have met inpatient criteria for other reasons based on case manager note review. CONCLUSION: This is the first time to our knowledge that a BPA has been used in this manner to reduce the number of Medicare 1-day write-offs.

Health care-associated Clostridioides difficile infection: Learning the perspectives of health care workers to build successful strategies.

BACKGROUND: Clostridioides difficile (C difficile) is one of the most common health care-associated infections that negatively impact patient care and health care costs. This study takes a unique approach to C difficile infection (CDI) control by investigating key prevention obstacles through the perspectives of Stanford health care (SHC) frontline health care personnel. METHODS: An anonymous qualitative survey was distributed at SHC, focusing on knowledge and practice of CDI prevention guidelines, as well as education, communication, and perspectives regarding CDI at SHC. RESULTS: 112 survey responses were analyzed. Our findings unveiled gaps in personnel's knowledge of C difficile diagnostic guidelines and revealed a need for targeted communication and guideline-focused education. Health care staff shared preferences and recommendations, with the majority recommending enhanced communication of guidelines and information as a strategy for reducing CDI rates. The findings were then used to design and propose internal recommendations for SHC to mitigate the gaps found. DISCUSSION: Many guidelines and improvement strategies are based on strong scientific and medical foundations; however, it is important to ask whether these guidelines are effectively translated into practice. Frontline health care workers hold empirical perspectives that could be key in infection control. CONCLUSIONS: Our findings emphasize the importance of including frontline health care personnel in infection prevention decision-making processes and the strategies presented here can be applied to mitigating infections in different health care settings.

Development and Initial Performance of the Hospital Mental Health Risk Screen.

BACKGROUND: Patients hospitalized after emergency care are at risk for later mental health problems such as depression, anxiety, and posttraumatic stress disorder symptoms. The American College of Surgeons Committee on Trauma standards for verification require Level I and II trauma centers to screen patients at high risk for mental health problems. This study aimed to develop and examine the performance of a novel mental health risk screen for hospitalized patients based on samples that reflect the diversity of the US population. STUDY DESIGN: We studied patients admitted after emergency care to 3 hospitals that serve ethnically, racially, and socioeconomically diverse populations. We assessed risk factors during hospitalization and mental health symptoms at follow-up. We conducted analyses to identify the most predictive risk factors, selected items to assess each risk, and determined the fewest items needed to predict mental health symptoms at follow-up. Analyses were conducted for the entire sample and within 5 ethnic and racial subgroups. RESULTS: Among 1,320 patients, 10 items accurately identified 75% of patients who later had elevated levels of mental health symptoms and 71% of those who did not. Screen performance was good to excellent within each of the ethnic and racial groups studied. CONCLUSIONS: The Hospital Mental Health Risk Screen accurately predicted mental health outcomes overall and within ethnic and racial subgroups. If performance is replicated in a new sample, the screen could be used to screen patients hospitalized after emergency care for mental health risk. Routine screening could increase health and mental health equity and foster preventive care research and implementation.

Lessons Learned from a Multi-Site, Team-Based Serious Illness Care Program Implementation at an Academic Medical Center.

Background: Patients with serious illness benefit from conversations to share prognosis and explore goals and values. To address this, we implemented Ariadne Labs' Serious Illness Care Program (SICP) at Stanford Health Care. Objective: Improve quantity, timing, and quality of serious illness conversations. Methods: Initial implementation followed Ariadne Labs' SICP framework. We later incorporated a team-based approach that included nonphysician care team members. Outcomes included number of patients with documented conversations according to clinician role and practice location. Machine learning algorithms were used in some settings to identify eligible patients. Results: Ambulatory oncology and hospital medicine were our largest implementation sites, engaging 4707 and 642 unique patients in conversations, respectively. Clinicians across eight disciplines engaged in these conversations. Identified barriers that included leadership engagement, complex workflows, and patient identification. Conclusion: Several factors contributed to successful SICP implementation across clinical sites: innovative clinical workflows, machine learning based predictive algorithms, and nonphysician care team member engagement.

A Large Language Model Screening Tool to Target Patients for Best Practice Alerts: Development and Validation.

BACKGROUND: Best Practice Alerts (BPAs) are alert messages to physicians in the electronic health record that are used to encourage appropriate use of health care resources. While these alerts are helpful in both improving care and reducing costs, BPAs are often broadly applied nonselectively across entire patient populations. The development of large language models (LLMs) provides an opportunity to selectively identify patients for BPAs. OBJECTIVE: In this paper, we present an example case where an LLM screening tool is used to select patients appropriate for a BPA encouraging the prescription of deep vein thrombosis (DVT) anticoagulation prophylaxis. The artificial intelligence (AI) screening tool was developed to identify patients experiencing acute bleeding and exclude them from receiving a DVT prophylaxis BPA. METHODS: Our AI screening tool used a BioMed-RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach; AllenAI) model to perform classification of physician notes, identifying patients without active bleeding and thus appropriate for a thromboembolism prophylaxis BPA. The BioMed-RoBERTa model was fine-tuned using 500 history and physical notes of patients from the MIMIC-III (Medical Information Mart for Intensive Care) database who were not prescribed anticoagulation. A development set of 300 MIMIC patient notes was used to determine the model's hyperparameters, and a separate test set of 300 patient notes was used to evaluate the screening tool. RESULTS: Our MIMIC-III test set population of 300 patients included 72 patients with bleeding (ie, were not appropriate for a DVT prophylaxis BPA) and 228 without bleeding who were appropriate for a DVT prophylaxis BPA. The AI screening tool achieved impressive accuracy with a precision-recall area under the curve of 0.82 (95% CI 0.75-0.89) and a receiver operator curve area under the curve of 0.89 (95% CI 0.84-0.94). The screening tool reduced the number of patients who would trigger an alert by 20% (240 instead of 300 alerts) and increased alert applicability by 14.8% (218 [90.8%] positive alerts from 240 total alerts instead of 228 [76%] positive alerts from 300 total alerts), compared to nonselectively sending alerts for all patients. CONCLUSIONS: These results show a proof of concept on how language models can be used as a screening tool for BPAs. We provide an example AI screening tool that uses a HIPAA (Health Insurance Portability and Accountability Act)-compliant BioMed-RoBERTa model deployed with minimal computing power. Larger models (eg, Generative Pre-trained Transformers-3, Generative Pre-trained Transformers-4, and Pathways Language Model) will exhibit superior performance but require data use agreements to be HIPAA compliant. We anticipate LLMs to revolutionize quality improvement in hospital medicine.

How Many Lives Will You Save? A Mixed Methods Evaluation of a Novel, Online Game for Patient Safety and Quality Improvement Education.

Medical trainees have limited knowledge of quality improvement and patient safety concepts. The authors developed a free quality improvement/patient safety educational game entitled Safety Quest (SQ). However, 1803 undergraduate medical trainees, graduate medical trainees, and continuing medical education learners globally completed at least 1 level of SQ. Pre- and post-SQ knowledge and satisfaction were assessed among continuing medical education learners. Thematic analysis of feedback given by trainees was conducted. Among graduate medical trainees, SQ outranked other learning modalities. Three content areas emerged from feedback: engagement, ease of use, and effectiveness; 87% of comments addressing engagement were positive. After completing SQ, 98.6% of learners passed the post-test, versus 59.2% for the pretest ( P < 0.0001). Ninety-three percent of learners agreed that SQ was engaging and interactive, and 92% believed it contributed to their professional growth. With an increased need for educational curricula to be delivered virtually, gamification emerges as a unique strategy that learners praise as engaging and effective.

Mental health symptoms are comparable in patients hospitalized with acute illness and patients hospitalized with injury.

BACKGROUND: High rates of mental health symptoms such as depression, anxiety, and posttraumatic stress disorder (PTSD) have been found in patients hospitalized with traumatic injuries, but little is known about these problems in patients hospitalized with acute illnesses. A similarly high prevalence of mental health problems in patients hospitalized with acute illness would have significant public health implications because acute illness and injury are both common, and mental health problems of depression, anxiety, and PTSD are highly debilitating. METHODS AND FINDINGS: In patients admitted after emergency care for Acute Illness (N = 656) or Injury (N = 661) to three hospitals across the United States, symptoms of depression, anxiety, and posttraumatic stress were compared acutely (Acute Stress Disorder) and two months post-admission (PTSD). Patients were ethnically/racially diverse and 54% female. No differences were found between the Acute Illness and Injury groups in levels of any symptoms acutely or two months post-admission. At two months post-admission, at least one symptom type was elevated for 37% of the Acute Illness group and 39% of the Injury group. Within racial/ethnic groups, PTSD symptoms were higher in Black patients with injuries than for Black patients with acute illness. A disproportionate number of Black patients had been assaulted. CONCLUSIONS: This study found comparable levels of mental health sequelae in patients hospitalized after emergency care for acute illness as in patients hospitalized after emergency care for injury. Findings of significantly higher symptoms and interpersonal violence injuries in Black patients with injury suggest that there may be important and actionable differences in mental health sequelae across ethnic/racial identities and/or mechanisms of injury or illness. Routine screening for mental health risk for all patients admitted after emergency care could foster preventive care and reduce ethnic/racial disparities in mental health responses to acute illness or injury.

Examining Diversity in Digital Therapeutics Clinical Trials: Descriptive Analysis.

BACKGROUND: Digital therapeutics (DTx) are an emerging class of software-based medical therapies helping to improve care access and delivery. As we leverage these digital health therapies broadly in clinical care, it is important to consider sociodemographic representation underlying clinical trials data to ensure broad application to all groups. OBJECTIVE: We review current sociodemographic representation in DTx clinical trials using data from the Digital Therapeutics Alliance Product Library database. METHODS: We conducted a descriptive analysis of DTx products. We analyzed 15 manuscripts associated with 13 DTx products. Sociodemographic information was retrieved and compared with the US population's demographic distribution. RESULTS: The median study size and age of participants were 252 and 43.3 years, respectively. Of the 15 studies applicable to this study, 10 (67%) reported that females made up 65% or greater of the study cohort. A total of 14 studies reported race data with Black or African American and Asian American individuals underrepresented in 9 and 11 studies, respectively. In 7 studies that reported ethnicity, Hispanics were underrepresented in all 7 studies. Furthermore, 8 studies reported education levels, with 5 studies reporting populations in which 70% or greater had at least some college education. Only 3 studies reported health insurance information, each reporting a study cohort in which 100% of members were privately insured. CONCLUSIONS: Our findings indicate opportunities for improved sociodemographic representation in DTx clinical trials, especially for underserved populations typically underrepresented in clinical trials. This review is a step in examining sociodemographic representation in DTx clinical trials to help inform the path forward for DTx development and testing.

Prospective pilot study of the Three Good Things positive psychology intervention in short-term stay hospitalised patients.

BACKGROUND: The 'Three Good Things' (3GT) positive psychology protocol developed at Duke University has been shown to decrease depressive symptoms and emotional exhaustion in healthcare providers. Whether hospitalised patients may also benefit from the 3GT protocol has not previously been explored. OBJECTIVES: To determine the impact and efficacy of the 3GT protocol with hospitalised patients experiencing serious/chronic illness. DESIGN: Patient-level randomised control trial. SETTING: Medical units of an academic, tertiary care medical centre. PATIENTS: 221 adults over the age of 18 years admitted to inpatient wards (intensive care units excluded) at Stanford Hospital between January 2017 and May 2018. INTERVENTIONS: Patients were randomised to the 3GT intervention arm or the control arm with no intervention. MEASUREMENTS AND MAIN RESULTS: There was no significant difference between the intervention and control groups in the primary outcomes of improved positivity scores, decreased negativity scores or increased positive-to-negative emotional ratios. CONCLUSIONS: A journal-based application of the 3GT protocol did not result in a statistically significant improvement in patient's emotional health.

Care to Share? Patients in Private Rooms Are More Likely to Recommend a Hospital to Others.

A patient's likelihood to recommend a hospital is used to assess the quality of their experience. This study investigated whether room type influences patients' likelihood to recommend Stanford Health Care using Hospital Consumer Assessment of Healthcare Providers and Systems survey data from November 2018 to February 2021 (n = 10,703). The percentage of patients who gave the top response was calculated as a top box score, and the effects of room type, service line, and the COVID-19 pandemic were represented as odds ratios (ORs). Patients in private rooms were more likely to recommend than patients in semi-private rooms (aOR: 1.32; 95% CI: 1.16-1.51; 86% vs 79%, p < .001), and service lines with only private rooms had the greatest increases in odds of a top response. The new hospital had significantly higher top box scores than the original hospital (87% vs 84%, p < .001), indicating that room type and hospital environment impact patients' likelihood to recommend.

Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study.

INTRODUCTION: Unnecessary laboratory testing contributes to patient morbidity and healthcare waste. Despite prior attempts at curbing such overutilization, there remains opportunity for improvement using novel data-driven approaches. This study presents the development and early evaluation of a clinical decision support tool that uses a predictive model to help providers reduce low-yield, repetitive laboratory testing in hospitalized patients. METHODS: We developed an EHR-embedded SMART on FHIR application that utilizes a laboratory test result prediction model based on historical laboratory data. A combination of semi-structured physician interviews, usability testing, and quantitative analysis on retrospective laboratory data were used to inform the tool's development and evaluate its acceptability and potential clinical impact. KEY RESULTS: Physicians identified culture and lack of awareness of repeat orders as key drivers for overuse of inpatient blood testing. Users expressed an openness to a lab prediction model and 13/15 physicians believed the tool would alter their ordering practices. The application received a median System Usability Scale score of 75, corresponding to the 75th percentile of software tools. On average, physicians desired a prediction certainty of 85% before discontinuing a routine recurring laboratory order and a higher certainty of 90% before being alerted. Simulation on historical lab data indicates that filtering based on accepted thresholds could have reduced âˆ¼22% of repeat chemistry panels. CONCLUSIONS: The use of a predictive algorithm as a means to calculate the utility of a diagnostic test is a promising paradigm for curbing laboratory test overutilization. An EHR-embedded clinical decision support tool employing such a model is a novel and acceptable intervention with the potential to reduce low-yield, repetitive laboratory testing.

Implementation and evaluation of an elective quality improvement curriculum for preclinical students: a prospective controlled study.

BACKGROUND: Quality improvement (QI) is a systematic approach to improving healthcare delivery with applications across all fields of medicine. However, exposure to QI is minimal in early medical education. We evaluated the effectiveness of an elective QI curriculum in teaching preclinical health professional students foundational QI concepts. METHODS: This prospective controlled cohort study was conducted at a single academic institution. The elective QI curriculum consisted of web-based video didactics and exercises, supplemented with in-person classroom discussions. An optional hospital-based QI project was offered. Assessments included pre- and post-intervention surveys evaluating QI skills and beliefs and attitudes, quizzes, and Quality Improvement Knowledge Application Tool-Revised (QIKAT-R) cases. Within-group pre-post and between-group comparisons were performed using descriptive statistics. RESULTS: Overall, 57 preclinical medical or physician assistant students participated under the QI curriculum group (N = 27) or control group (N = 30). Twenty-three (85%) curriculum students completed a QI project. Mean quiz scores were significantly improved in the curriculum group from pre- to post-assessment (Quiz 1: 2.0, P < 0.001; Quiz 2: 1.7, P = 0.002), and the mean differences significantly differed from those in the control group (Quiz 1: P < 0.001; Quiz 2: P = 0.010). QIKAT-R scores also significantly differed among the curriculum group versus controls (P = 0.012). In the curriculum group, students had improvements in their confidence with all 10 QI skills assessed, including 8 that were significantly improved from pre- to post-assessment, and 4 with significant between-group differences compared with controls. Students in both groups agreed that their medical education would be incomplete without a QI component and that they are likely to be involved in QI projects throughout their medical training and practice. CONCLUSIONS: The elective QI curriculum was effective in guiding preclinical students to develop their QI knowledge base and skillset. Preclinical students value QI as an integral component of their medical training. Future directions involve evaluating the impact of this curriculum on clinical clerkship performance and across other academic institutions.

Racial/ethnic differences in acute and longer-term posttraumatic symptoms following traumatic injury or illness.

BACKGROUND: Racial/ethnic differences in mental health outcomes after a traumatic event have been reported. Less is known about factors that explain these differences. We examined whether pre-, peri-, and post-trauma risk factors explained racial/ethnic differences in acute and longer-term posttraumatic stress disorder (PTSD), depression, and anxiety symptoms in patients hospitalized following traumatic injury or illness. METHODS: PTSD, depression, and anxiety symptoms were assessed during hospitalization and 2 and 6 months later among 1310 adult patients (6.95% Asian, 14.96% Latinx, 23.66% Black, 4.58% multiracial, and 49.85% White). Individual growth curve models examined racial/ethnic differences in PTSD, depression, and anxiety symptoms at each time point and in their rate of change over time, and whether pre-, peri-, and post-trauma risk factors explained these differences. RESULTS: Latinx, Black, and multiracial patients had higher acute PTSD symptoms than White patients, which remained higher 2 and 6 months post-hospitalization for Black and multiracial patients. PTSD symptoms were also found to improve faster among Latinx than White patients. Risk factors accounted for most racial/ethnic differences, although Latinx patients showed lower 6-month PTSD symptoms and Black patients lower acute and 2-month depression and anxiety symptoms after accounting for risk factors. Everyday discrimination, financial stress, past mental health problems, and social constraints were related to these differences. CONCLUSION: Racial/ethnic differences in risk factors explained most differences in acute and longer-term PTSD, depression, and anxiety symptoms. Understanding how these risk factors relate to posttraumatic symptoms could help reduce disparities by facilitating early identification of patients at risk for mental health problems.

Considerations in the reliability and fairness audits of predictive models for advance care planning.

Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend that models be audited for reliability and fairness. However, there is a gap of operational guidance for performing reliability and fairness audits in practice. Following guideline recommendations, we conducted a reliability audit of two models based on model performance and calibration as well as a fairness audit based on summary statistics, subgroup performance and subgroup calibration. We assessed the Epic End-of-Life (EOL) Index model and an internally developed Stanford Hospital Medicine (HM) Advance Care Planning (ACP) model in 3 practice settings: Primary Care, Inpatient Oncology and Hospital Medicine, using clinicians' answers to the surprise question ("Would you be surprised if [patient X] passed away in [Y years]?") as a surrogate outcome. For performance, the models had positive predictive value (PPV) at or above 0.76 in all settings. In Hospital Medicine and Inpatient Oncology, the Stanford HM ACP model had higher sensitivity (0.69, 0.89 respectively) than the EOL model (0.20, 0.27), and better calibration (O/E 1.5, 1.7) than the EOL model (O/E 2.5, 3.0). The Epic EOL model flagged fewer patients (11%, 21% respectively) than the Stanford HM ACP model (38%, 75%). There were no differences in performance and calibration by sex. Both models had lower sensitivity in Hispanic/Latino male patients with Race listed as "Other." 10 clinicians were surveyed after a presentation summarizing the audit. 10/10 reported that summary statistics, overall performance, and subgroup performance would affect their decision to use the model to guide care; 9/10 said the same for overall and subgroup calibration. The most commonly identified barriers for routinely conducting such reliability and fairness audits were poor demographic data quality and lack of data access. This audit required 115 person-hours across 8-10 months. Our recommendations for performing reliability and fairness audits include verifying data validity, analyzing model performance on intersectional subgroups, and collecting clinician-patient linkages as necessary for label generation by clinicians. Those responsible for AI models should require such audits before model deployment and mediate between model auditors and impacted stakeholders.

Explaining emergency physicians' capacity to recover from interruptions.

OBJECTIVE: To assess whether the capacity to utilize cues amongst emergency physicians is associated with differences in the capacity to recover performance following an interruption. BACKGROUND: Interruptions are implicated in errors in emergency medicine due to the cognitive load that they impose on working memory, resulting in a loss of performance on the primary task. The utilization of cues is associated with a reduction in cognitive load during the performance of a task, thereby enabling the allocation of residual resources that mitigates the loss of performance following interruptions. METHOD: Thirty-nine emergency physicians, recruited at a medical conference, completed an assessment of cue utilization (EXPERTise 2.0) and an online simulation (Septris) that involved the management of patients presenting with sepsis. During the simulation, physicians were interrupted and asked to check a medication order. Task performance was assessed using scores on Septris, with points awarded for the accurate management of patients. RESULTS: Emergency physicians with higher cue utilization recorded significantly higher scores on the simulation task following the interruption, compared to physicians with lower cue utilization (p = .028). CONCLUSION: The results confirm a relationship between cue utilization and the recovery of performance following an interruption. This is likely due to the advantages afforded by associated reductions in cognitive load. APPLICATION: Assessments of cue utilization may assist in the development of interventions to support clinicians in interruptive environments.

Assessment of level of care recommendations and nursing acuity scores following an appropriateness of care intervention.

BACKGROUND: As part of a multiprong intervention to eliminate waste in cost of hospital accommodations, the InterQual Level of Care (LOC) criteria was deployed by our institution to assign patients to one of three LOCs: acute care, intermediate intensive care unit (IICU) or intensive care unit (ICU). In that intervention, which sought to decrease the number of patients in a higher LOC than what was clinically necessary, patient safety balancing metrics were stable. However, nursing workload, a key balancing metric, has yet to be examined. In this study, we examine nursing workload before and after the intervention using a proprietary nursing acuity score. METHODS: A retrospective study was conducted analysing admissions at the study institution. Patient's LOC recommendation (as determined by InterQual), assigned (actual) LOC and nursing acuity scores were collected and analysed. Average nursing acuity scores were compared across patients whose InterQual recommendation aligned with actual LOC ('Acute Match' or 'IICU Match') versus patients who were recommended to be in acute care but were receiving IICU care ('Mismatch'). RESULTS: Following the intervention, the per cent of patients in the Mismatch cohort decreased from 13% to 7%. Prior to the intervention, average nursing acuity score for the Mismatch cohort was less than the IICU Match cohort and greater than Acute Match cohort in all departments analysed. After the intervention period, average acuity score in the Mismatch cohort exceeded that of the Acute Match cohort in all eight departments, but the Mismatch cohort's scores differed from the IICU Match cohort in only one department. CONCLUSION: Collectively, this study demonstrates that our intervention successfully decreased inappropriate use of the IICU LOC, and that the residual Mismatch cohort is a distinct entity, with nursing needs that exceed that of the Acute Match cohort. Thus, a higher LOC can be justified. This demonstrates that a nursing workload metric such as the nursing acuity score can be a valuable complement to clinical criteria such as the InterQual LOC criteria to objectively determine patient's true, necessary LOC and ensure that nursing staff feels adequately staffed to care for patients.

Patient Perspectives of Inpatient Telemedicine During the COVID-19 Pandemic: Qualitative Assessment.

BACKGROUND: Telemedicine has been adopted in the inpatient setting to facilitate clinical interactions between on-site clinicians and isolated hospitalized patients. Such remote interactions have the potential to reduce pathogen exposure and use of personal protective equipment but may also pose new safety concerns given prior evidence that isolated patients can receive suboptimal care. Formal evaluations of the use and practical acceptance of inpatient telemedicine among hospitalized patients are lacking. OBJECTIVE: We aimed to evaluate the experience of patients hospitalized for COVID-19 with inpatient telemedicine introduced as an infection control measure during the pandemic. METHODS: We conducted a qualitative evaluation in a COVID-19 designated non-intensive care hospital unit at a large academic health center (Stanford Health Care) from October 2020 through January 2021. Semistructured qualitative interviews focused on patient experience, impact on quality of care, communication, and mental health. Purposive sampling was used to recruit participants representing diversity across varying demographics until thematic saturation was reached. Interview transcripts were qualitatively analyzed using an inductive-deductive approach. RESULTS: Interviews with 20 hospitalized patients suggested that nonemergency clinical care and bridging to in-person care comprised the majority of inpatient telemedicine use. Nurses were reported to enter the room and call on the tablet far more frequently than physicians, who typically entered the room at least daily. Patients reported broad acceptance of the technology, citing improved convenience and reduced anxiety, but preferred in-person care where possible. Quality of care was believed to be similar to in-person care with the exception of a few patients who wanted more frequent in-person examinations. Ongoing challenges included low audio volume, shifting tablet location, and inconsistent verbal introductions from the clinical team. CONCLUSIONS: Patient experiences with inpatient telemedicine were largely favorable. Although most patients expressed a preference for in-person care, telemedicine was acceptable given the circumstances associated with the COVID-19 pandemic. Improvements in technical and care team use may enhance acceptability. Further evaluation is needed to understand the impact of inpatient telemedicine and the optimal balance between in-person and virtual care in the hospital setting.

A randomized study of a best practice alert for platelet transfusions.

BACKGROUND AND OBJECTIVES: Inappropriate platelet transfusions represent an opportunity for improvements in patient care. Use of a best practice alert (BPA) as clinical decision support (CDS) for red cell transfusions has successfully reduced unnecessary red blood cell (RBC) transfusions in prior studies. We studied the impact of a platelet transfusion BPA with visibility randomized by patient chart. MATERIALS AND METHODS: A BPA was built to introduce CDS at the time of platelet ordering in the electronic health record. Alert visibility was randomized at the patient encounter level. BPA eligible platelet transfusions for patients with both visible and non-visible alerts were recorded along with reasons given for override of the BPA. Focused interviews were performed with providers who interacted with the BPA to assess its impact on their decision making. RESULTS: Over a 9-month study period, 446 patient charts were randomized. The visible alert group used 25.3% fewer BPA eligible platelets. Mean monthly usage of platelets eligible for BPA display was 65.7 for the control group and 49.1 for the visible alert group (p = 0.07). BPA-eligible platelets used per inpatient day at risk per month were not significantly different between groups (2.4 vs. 2.1, p = 0.53). CONCLUSION: It is feasible to study CDS via chart-based randomization. A platelet BPA reduced total platelets used over the study period and may have resulted in $151,069 in yearly savings, although there were no differences when adjusted for inpatient days at risk. During interviews, providers offered additional workflow insights allowing further improvement of CDS for platelet transfusions.

Prospective pilot study of the Three Good Things positive psychology intervention in short-term stay hospitalised patients.

BACKGROUND: The 'Three Good Things' (3GT) positive psychology protocol developed at Duke University has been shown to decrease depressive symptoms and emotional exhaustion in healthcare providers. Whether hospitalised patients may also benefit from the 3GT protocol has not previously been explored. OBJECTIVES: To determine the impact and efficacy of the 3GT protocol with hospitalised patients experiencing serious/chronic illness. DESIGN: Patient-level randomised control trial. SETTING: Medical units of an academic, tertiary care medical centre. PATIENTS: 221 adults over the age of 18 years admitted to inpatient wards (intensive care units excluded) at Stanford Hospital between January 2017 and May 2018. INTERVENTIONS: Patients were randomised to the 3GT intervention arm or the control arm with no intervention. MEASUREMENTS AND MAIN RESULTS: There was no significant difference between the intervention and control groups in the primary outcomes of improved positivity scores, decreased negativity scores or increased positive-to-negative emotional ratios. CONCLUSIONS: A journal-based application of the 3GT protocol did not result in a statistically significant improvement in patient's emotional health.