Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital.

BACKGROUND: The increasing aging population presents a significant challenge, accompanied by a shortage of professional caregivers, adding to the therapeutic burden. Clinical decision support systems, utilizing computerized clinical guidelines, can improve healthcare quality, reduce expenses, save time, and boost caregiver efficiency. OBJECTIVES: 1) Develop and evaluate an automated quality assessment (QA) system for retrospective longitudinal care quality analysis, focusing on clinical staff adherence to evidence-based guidelines (GLs). 2) Assess the system's technical feasibility and functional capability for senior nurse use in geriatric pressure-ulcer management. METHODS: A computational QA system using our Quality Assessment Temporal Patterns (QATP) methodology was designed and implemented. Our methodology transforms the GL's procedural-knowledge into declarative-knowledge temporal-abstraction patterns representing the expected execution trace in the patient's data for correct therapy application. Fuzzy temporal logic allows for partial compliance, reflecting individual and grouped action performance considering their values and temporal aspects. The system was tested using a pressure ulcer treatment GL and data from 100 geriatric patients' Electronic Medical Records (EMR). After technical evaluation for accuracy and feasibility, an extensive functional evaluation was conducted by an experienced nurse, comparing QA scores with and without system support, and versus automated system scores. Time efficiency was also measured. RESULTS: QA scores from the geriatric nurse, with and without system's support, did not significantly differ from those provided by the automated system (p < 0.05), demonstrating the effectiveness and reliability of both manual and automated methods. The system-supported manual QA process reduced scoring time by approximately two-thirds, from an average of 17.3 min per patient manually to about 5.9 min with the system's assistance, highlighting the system's efficiency potential in clinical practice. CONCLUSION: The QA system based on QATP, produces scores consistent with an experienced nurse's assessment for complex care over extended periods. It enables quick and accurate quality care evaluation for multiple patients after brief training. Such automated QA systems may empower nursing staff, enabling them to manage more patients, accurately and consistently, while reducing costs due to saved time and effort, and enhanced compliance with evidence-based guidelines.

Validation of Quality Assessment Measures for Inpatient Gastroenterology Consults on Oncologic Patients in Non-teaching Services at a Cancer Center: A Cross-Sectional Study.

OBJECTIVE: To develop and validate tools for measuring inpatient gastroenterology (GI) consultation quality on oncologic patients. METHODS: A total of 145 inpatient GI consults were analyzed using electronic health records in this cross-sectional study. Essential Consult Elements on oncologic-hospitalized patients (EE-COH) and Hospitalized Oncologic Patients Enhanced Quality of Consult Assessment Tool (HOPE-QCAT) were used for grading. Interrater reliability was assessed. RESULTS: Both EE-COH and HOPE-QCAT showed near-perfect interrater reliability across most measures in the validation cohort. On application of these measures for quality assessment, basic evaluation by the requesting hospitalist was partially complete in 24.8%, the request for GI consultation was inappropriate in 18.6%, while the rationale for recommended studies from the GI consultant was provided in 55.7% of cases suggesting key areas for quality improvement. CONCLUSION: We developed highly reliable quality measures for inpatient GI consults on oncology patients. The EE-COH and HOPE-QCAT tools can be utilized in future studies of inpatient GI consult quality and to form the basis for interventions to improve communication between consultants and hospitalists. Such tools could be adapted for inpatient quality assessment across other specialties and settings.

Developing a quality and safety assessment framework for Iran's military hospitals.

BACKGROUND: The first crucial step towards military hospitals performance improvement is to develop a local and scientific tool to assess quality and safety based on the context and aims of military hospitals. This study introduces a Quality and Safety Assessment Framework (Q&SAF) for Iran's military hospitals. METHODS: This is a literature review which continued with a qualitative study. The Q&SAF for Iran's military hospitals was developed initially, through a review of the WHO's framework for hospital performance, literature review (other related framework), review of military hospital-related local documents, consultations with a national and sub-national expert. Finally, the Delphi technique used to finalize the framework. RESULTS: Based on the literature review results; 13 hospital Q&SAF were identified. After reviewing literature review results and expert opinions; Iran's military hospitals Q&SAF was developed with 58 indictors in five dimensions including clinical effectiveness, safety, efficiency, patient-centeredness, and Responsive Management (Command and Control). The efficiency dimension had the highest number of indictors (19 indictors), whereas the patient-centered dimension had the lowest number of indices (4 indictors). CONCLUSION: Regarding the comprehensiveness of the developed assessment framework due to its focus on the majority of quality dimensions and important components of the hospital's performance, it can be used as a useful tool for assessing and continuously improving the quality of hospitals, particularly military hospitals.

An evaluation of the quality care for type 2 diabetes patients in the primary healthcare using the lot quality assurance sampling technique.

BACKGROUND: Diabetes is the most prevalent metabolic disease globally. Correct and effective healthcare management requires up-to-date and accurate information at the local level. This level of information allows managers to determine whether the health system has achieved its desired goals in this area. This study aimed to evaluate the adequacy and quality of care for Type 2 diabetes mellitus (T2DM) patients using the Lot quality assurance sampling (LQAS) technique to provide evidence for decision-making at the local level, prioritizing and allocating resources. METHODS: A descriptive-analytical study was conducted in 12 supervision areas (SAs)/health facilities in northwestern Iran involving 240 patients with T2DM in primary health care. The selection of patients and determination of SAs were done randomly using the LQAS technique. Glycated Hemoglobin (HbA1c) was used to evaluate patients' blood sugar control in each SA. Multiple linear regression analysis was used to estimate predictors of HbA1c in T2DM. RESULTS: The overall average of HbA1c value was 7.84%. The HbA1c level was > 7% in 148 (61.6%) of the patients. Among the 12 SAs, the LQAS identified unacceptable quality of care in 5 SAs. In the final analysis, each unit increase in fasting blood sugar (FBS), High-density lipoprotein (HDL), Low-density lipoprotein (LDL), and Thyroglobulin (TG) values resulted in an increased in HbA1c levels by 0.43, 0.183, 0.124, and 0.182 times, respectively. However, with a one-unit increase in the care of a family physician and nutritionist, along with regular physical activity, HbA1c levels decreased by - 0.162, -0.74, and - 0.11 times, respectively. CONCLUSIONS: The quality of care for diabetic patients needs improvement in some SAs. Findings indicated that the LQAS technique effectively identifies centers/areas with substandard diabetes care quality and efficiently allocates resources to those in need. It is recommended to implement corrective measures in areas with inadequate care quality.

Point-of-Care-Ultrasound Quality Assurance Data From Fellow-Performed Exams in a Pediatric Emergency Department: A Descriptive Study.

OBJECTIVES: Point-of-care-ultrasound (POCUS) is increasingly used by pediatric emergency medicine (PEM) fellows, but scant data exists on the accuracy of exam interpretations. Our goal was to determine whether agreement on exam interpretation between quality assurance (QA) faculty (reference standard) and PEM fellows varied by fellowship year or exam type. METHODS: Retrospective review of fellow-performed POCUS exams between January 2019 and June 2022. Negative binomial (NB) random effects regression was used to account for longitudinal measurement of individual fellow performance across 3 years. Fixed effects were exam type and fellowship year. To assess between- and within-user variability across time, a random intercept and slope were included for each fellow. RESULTS: Exactly 3032 exams, performed by 24 fellows, were included. Raw proportion agreement by fellowship year was high for all exam types (≥88%). From the NB model, there was no statistically significant effect of fellowship year on the mean count of agreement. The relative risk (RR) of agreement for exam types was greatest for cardiac vs other types. The standard deviations for the random intercept and random slope were 0.09 and 0.04, respectively, with a correlation of -0.94. CONCLUSIONS: PEM fellows generally interpret exams correctly, with little variation through fellowship, although those who began with more basic skills showed more progress over time. Fellowship year did not influence the likelihood of correct interpretation but there was variation across exam type, with the best agreement for cardiac exams. The extent to which disagreements between fellows and QA faculty represent clinically significant errors requires further study.

A lot testing protocol for quality assurance of fentanyl test strips for harm reduction applications.

Fentanyl test strips (FTS) are lateral flow immunoassays that were originally designed and validated for detecting low concentrations of fentanyl in urine. Some FTS are now being marketed for the harm reduction purpose of testing street drugs for the presence of fentanyl. This manuscript provides a simple protocol to assess whether different brands and lots of fentanyl test strips perform adequately for use in drug checking. The results gathered from this protocol will document problems with particular lots or brands of FTS, help buyers choose from among the array of products, provide feedback to manufacturers to improve their products, and serve as an early warning system for ineffective products.

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy.

PURPOSE: To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. METHODS: The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis. RESULTS: The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365. CONCLUSIONS: The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

Predicting the error magnitude in patient-specific QA during radiotherapy based on ResNet.

BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

Dosimetry evaluation of photon beam profile characteristics for different treatment parameters of quality assurance.

OBJECTIVE: To compare beam profiles of MatriXX scanning system and water phantom for different treatment parameters. METHODS: The cross-sectional study was conducted at Al-Amal National Hospital for Cancer Treatment, Baghdad, Iraq, from November 2020 to March 2021. Beam data for 6MV and 10MV photon beams generated from the linear accelerator was utilised at field sizes 20×20cm2, 15×15 cm2, 10×10cm2 and 5×5cm2 at depth 10 and source-to-skin distance 100cm. Data was obtained for both water phantom and MatriXX system. The dose distribution for the two systems were compared. Data was analysed using SPSS 24. RESULTS: The 32 measures taken were all related to symmetry and flatness. Flatness data indicated that all measurements were within tolerance except for cross line plane variations in 10x10cm2 field size with 6MV energy (-3.81%) and 5x5cm2 field size with 10MV energy (-3.01).Symmetry data revealed all measurement differences were within tolerance. CONCLUSIONS: MatriXX system could also be used for routine photon profile measurements as a substitute for water phantom.

Evaluation of a high resolution diode array for CyberKnife quality assurance.

PURPOSE: The CyberKnife quality assurance (QA) program relies mainly on the use of radiochromic film (RCF). We aimed at evaluating high-resolution arrays of detectors as an alternative to films for CyberKnife machine QA. METHODS: This study will test the SRS Mapcheck (Sun Nuclear, Melbourne, Florida, USA) diode array and its own software, which allows three tests of the CyberKnife QA program to be performed. The first one is a geometrical accuracy test based on the delivery of two orthogonal beams (Automated Quality Assurance, AQA). Besides comparing the constancy and repeatability of both methods, known errors will be introduced to check their sensitivity. The second checks the constancy of the iris collimator field sizes (Iris QA). Changes in the field sizes will be introduced to study the array sensitivity. The last test checks the correct positioning of the multileaf collimator (MLC). It will be tested introducing known systematic displacements to whole banks and to single leaves. RESULTS: The results of the RCF and diode array were equivalent (maximum differences of 0.18 ± 0.14 mm) for the AQA test, showing the array a higher reproducibility. When known errors were introduced, both methods behaved linearly with similar slopes. Regarding Iris QA, the array measurements are highly linear when changes in the field sizes are introduced. Linear regressions show slopes of 0.96-1.17 with r2 above 0.99 in all field sizes. Diode array seems to detect changes of 0.1 mm. In MLC QA, systematic errors of the whole bank of leaves were not detected by the array, while single leaf errors were detected. CONCLUSIONS: The diode array is sensitive and accurate in the AQA and Iris QA tests, which give us the possibility of substituting RCF with a diode array. QA would be performed faster than using the film procedure, obtaining reliable results. Regarding the MLC QA, the inability to detect systematic displacements make it difficult to confidently use the detector.

[Quality characteristics and relevance to care of psychodynamic training outpatient clinics:The QVA Project]. / Qualitätsmerkmale und Versorgungsrelevanz psychodynamischer Ausbildungsambulanzen: Das QVA-Projekt.

Introduction: Quality assurance (QA) in outpatient psychotherapy is currently undergoing a process of change. Hitherto, QA has been conducted by means of an expert review procedure (the so-called "Gutachterverfahren"), inter- and supervision as well as further mandatory training. Data-based QA systems have been increasingly discussed in recent years. On behalf of the G-BA, the IQTIG has recently published a draft of a legally binding QA procedure, which has, however, raised substantial concerns and resistance. Design: TheQVA project has two objectives. First, it provides participating training outpatient clinics with a data-driven QA system that enables an automated and risk-adjusted overall evaluation based on relevant patient and referral parameters. Second, the data is used to conduct research on important issues regarding the relevant psychotherapeutic care provided by outpatient clinics. Results: Since the start of data collection in 2022, n = 2058 patients have been recruited so far (March 2023), and a complete baseline diagnostic report has been generated for n = 1112 patients. The cross-sectional analyses of all patients assessed so far show a high burden of depression, interpersonal problems and impaired quality of life with severe impairment of personality functions, pronounced conflict diagnosis and high utilization of inpatient and day hospital treatments. Discussion: This paper describes an easy-to-implement data-based QA system for psychodynamic training outpatient clinics, while at the same time allowing for the examination of healthcare- relevant questions in a large sample. The first experiences show that the system works technically stable and was well-received by the participating outpatient clinics.

Development of a Reliable Surgical Quality Assurance System for 2-stage Esophagectomy in Randomized Controlled Trials.

OBJECTIVE: The aim was to develop a reliable surgical quality assurance system for 2-stage esophagectomy. This development was conducted during the pilot phase of the multicenter ROMIO trial, collaborating with international experts. SUMMARY OF BACKGROUND DATA: There is evidence that the quality of surgical performance in randomized controlled trials influences clinical outcomes, quality of lymphadenectomy and loco-regional recurrence. METHODS: Standardization of 2-stage esophagectomy was based on structured observations, semi-structured interviews, hierarchical task analysis, and a Delphi consensus process. This standardization provided the structure for the operation manual and video and photographic assessment tools. Reliability was examined using generalizability theory. RESULTS: Hierarchical task analysis for 2-stage esophagectomy comprised fifty-four steps. Consensus (75%) agreement was reached on thirty-nine steps, whereas fifteen steps had a majority decision. An operation manual and record were created. A thirty five-item video assessment tool was developed that assessed the process (safety and efficiency) and quality of the end product (anatomy exposed and lymphadenectomy performed) of the operation. The quality of the end product section was used as a twenty seven-item photographic assessment tool. Thirty-one videos and fifty-three photographic series were submitted from the ROMIO pilot phase for assessment. The overall G-coefficient for the video assessment tool was 0.744, and for the photographic assessment tool was 0.700. CONCLUSIONS: A reliable surgical quality assurance system for 2-stage esophagectomy has been developed for surgical oncology randomized controlled trials. ETHICAL APPROVAL: 11/NW/0895 and confirmed locally as appropriate, 12/SW/0161, 16/SW/0098.Trial registration number: ISRCTN59036820, ISRCTN10386621.

Face-Validated Quality Indicators for Appropriateness of End-of-Life Care in Children with Serious Illness: A Study Using the RAND/University of California at Los Angeles Appropriateness Method.

OBJECTIVE: To develop and face-validate population-level indicators for potential appropriateness of end-of-life care, for children with cancer, neurologic conditions, and genetic/congenital conditions, to be applied to administrative health data containing medication and treatment variables. STUDY DESIGN: Modified RAND/University of California at Los Angeles appropriateness method. We identified potential indicators per illness group through systematic literature review, scoping review, and expert interviews. Three unique expert panels, a cancer (n = 19), neurology (n = 21), and genetic/congenital (n = 17) panel, participated in interviews and rated indicators in individual ratings, group discussions, and second individual ratings. Each indicator was rated on a scale from 1 to 9 for suitability. Consensus was calculated with the interpercentile range adjusted for symmetry formula. Indicators with consensus about unsuitability were removed, those with consensus about suitability were retained, and those with lack of consensus deliberated in the group discussion. Experts included pediatricians, nurses, psychologists, physiotherapists, pharmacologists, care coordinators, general practitioners, social workers from hospitals, care teams, and general practice. RESULTS: Literature review and expert interviews yielded 115 potential indicators for cancer, 111 for neurologic conditions, and 99 for genetic/congenital conditions. We combined similar indicators, resulting in respectively 36, 32, and 33 indicators per group. Expert scoring approved 21 indicators for cancer, 24 for neurologic conditions, and 23 for genetic/congenital conditions. CONCLUSIONS: Our indicators can be applied to administrative data to evaluate appropriateness of children's end-of-life care. Differences from adults' indicators stress the specificity of children's end-of-life care. Individual care and remaining aspects, such as family support, can be evaluated with complementary tools.

Assessing a New Prescreening Score for the Simplified Evaluation of the Clinical Quality and Relevance of eHealth Apps: Instrument Validation Study.

BACKGROUND: In 2020, more than 250 eHealth solutions were added to app stores each day, or 90,000 in the year; however, the vast majority of these solutions have not undergone clinical validation, their quality is unknown, and the user does not know if they are effective and safe. We sought to develop a simple prescreening scoring method that would assess the quality and clinical relevance of each app. We designed this tool with 3 health care stakeholder groups in mind: eHealth solution designers seeking to evaluate a potential competitor or their own tool, investors considering a fundraising candidate, and a hospital clinician or IT department wishing to evaluate a current or potential eHealth solution. OBJECTIVE: We built and tested a novel prescreening scoring tool (the Medical Digital Solution scoring tool). The tool, which consists of 26 questions that enable the quick assessment and comparison of the clinical relevance and quality of eHealth apps, was tested on 68 eHealth solutions. METHODS: The Medical Digital Solution scoring tool is based on the 2021 evaluation criteria of the French National Health Authority, the 2022 European Society of Medical Oncology recommendations, and other provided scores. We built the scoring tool with patient association and eHealth experts and submitted it to eHealth app creators, who evaluated their apps via the web-based form in January 2022. After completing the evaluation criteria, their apps obtained an overall score and 4 categories of subscores. These criteria evaluated the type of solution and domain, the solution's targeted population size, the level of clinical assessment, and information about the provider. RESULTS: In total, 68 eHealth solutions were evaluated with the scoring tool. Oncology apps (22%, 20/90) and general health solutions (23%, 21/90) were the most represented. Of the 68 apps, 32 (47%) were involved in remote monitoring by health professionals. Regarding clinical outcomes, 5% (9/169) of the apps assessed overall survival. Randomized studies had been conducted for 21% (23/110) of the apps to assess their benefit. Of the 68 providers, 38 (56%) declared the objective of obtaining reimbursement, and 7 (18%) out of the 38 solutions seeking reimbursement were assessed as having a high probability of reimbursement. The median global score was 11.2 (range 4.7-17.4) out of 20 and the distribution of the scores followed a normal distribution pattern (Shapiro-Wilk test: P=.33). CONCLUSIONS: This multidomain prescreening scoring tool is simple, fast, and can be deployed on a large scale to initiate an assessment of the clinical relevance and quality of a clinical eHealth app. This simple tool can help a decision-maker determine which aspects of the app require further analysis and improvement.

Assessment of National External Quality Assurance Programme of Pakistan as a tool for improving quality of lab results among participating laboratories.

OBJECTIVE: To assess the impact of the National External Quality Assessment Programme of Pakistan NEQAPP in improving the quality of laboratory results among the participating laboratories. METHODS: The cross-sectional observational study was conducted from July to December 2020 at the Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology, Rawalpindi, Pakistan, in association with the National Quality Assurance Programme of Pakistan. A survey questionnaire was developed and sent to the participating laboratories via email. Frequencies of their responses were calculated and data was analysed using SPSS 21. RESULTS: Of the 150 laboratories approached, 145(96.6%) responded. Among them, 140 (96.6%) laboratories were satisfied by the information provided on the programme's portal, 123(84.8%s) were pleased with the responsiveness of the programme manager, 140(96.6%) reported quality of services had improved after participation in the programme, 129(89%) indicated that the clinician's confidence had enhanced, and 122(84%) said the participation in the programme had improved the credibility of their respective of laboratories. CONCLUSIONS: The National External Quality Assessment Programme of Pakistan was found to have significantly contributed in improving the quality of laboratory results among the participating laboratories.

Quality improvement for cancer multidisciplinary teams: lessons learned from the Anglian Germ Cell Cancer Collaborative Group.

Shamash and colleagues describe how their supra-regional germ cell tumour multidisciplinary team achieved standardisation of treatment and improved survival. We discuss some of the insights the study provides into prioritising complex patients, streamlining processes, the use of telemedicine, and the centrality of good data collection to continuous quality improvement.

Opening Pandora's Box: From Readmissions to Transitional Care Patient-Centered Outcome Measures.

BACKGROUND: Measuring the effectiveness of transitional care interventions has historically relied on health care utilization as the primary outcome. Although the Care Transitions Measure was the first outcome measure specifically developed for transitional care, its applicability beyond the hospital-to-home transition is limited. There is a need for patient-centered outcome measures (PCOMs) to be developed for transitional care settings (ie, TC-PCOMs) to ensure that outcomes are both meaningful to patients and relevant to the particular care transition. The overall objective of this paper is to describe the opportunities and challenges of integrating TC-PCOMs into research and practice. METHODS AND RESULTS: This narrative review was conducted by members of the Patient-Centered Outcomes Research Institute (PCORI) Transitional Care Evidence to Action Network. We define TC-PCOMs as outcomes that matter to patients because they account for their individual experiences, concerns, preferences, needs, and values during the transition period. The cardinal features of TC-PCOMs should be that they are developed following direct input from patients and stakeholders and reflect their lived experience during the transition in question. Although few TC-PCOMs are currently available, existing patient-reported outcome measures could be adapted to become TC-PCOMs if they incorporated input from patients and stakeholders and are validated for the relevant care transition. CONCLUSION: Establishing validated TC-PCOMs is crucial for measuring the responsiveness of transitional care interventions and optimizing care that is meaningful to patients.

Implementation of Complex Interventions: Lessons Learned From the Patient-Centered Outcomes Research Institute Transitional Care Portfolio.

BACKGROUND: Despite the well-documented risks to patient safety associated with transitions from one care setting to another, health care organizations struggle to identify which interventions to implement. Multiple strategies are often needed, and studying the effectiveness of these complex interventions is challenging. OBJECTIVE: The objective of this study was to present lessons learned in implementing and evaluating complex transitional care interventions in routine clinical care. RESEARCH DESIGN: Nine transitional care study teams share important common lessons in designing complex interventions with stakeholder engagement, implementation, and evaluation under pragmatic conditions (ie, using only existing resources), and disseminating findings in outlets that reach policy makers and the people who could ultimately benefit from the research. RESULTS: Lessons learned serve as a guide for future studies in 3 areas: (1) Delineating the function (intended purpose) versus form (prespecified modes of delivery of the intervention); (2) Evaluating both the processes supporting implementation and the impact of adaptations; and (3) Engaging stakeholders in the design and delivery of the intervention and dissemination of study results. CONCLUSION: These lessons can help guide future pragmatic studies of care transitions.

A Novel Use of Artificial Intelligence to Examine Diversity and Hospital Performance.

BACKGROUND: The US population is becoming more racially and ethnically diverse. Research suggests that cultural diversity within organizations can increase team potency and performance, yet this theory has not been explored in the field of surgery. Furthermore, when surveyed, patients express a desire for their care provider to mirror their own race and ethnicity. In the present study, we hypothesize that there is a positive correlation between a high ranking by the US News and World Report for gastroenterology and gastrointestinal (GI) surgery and greater racial, ethnic, and gender diversity among the physicians and surgeons. METHODS: We used the 2019 US News and World Report rankings for best hospitals by specialty to categorize gastroenterology and GI surgery departments into 2 groups: 1-50 and 51-100. Hospital websites of these top 100 were viewed to determine if racial diversity and inclusion were highlighted in the hospitals' core values or mission statements. To determine the rates of diversity within departments, Betaface (Betaface.com) facial analysis software was used to analyze photos taken from the hospitals' websites. This software was able to determine the race, ethnicity, and gender of the care providers. We examined the racial and ethnic makeup of the populations served by these hospitals to see if the gastroenterologists and surgeons adequately represented the state population. We then ran the independent samples t-test to determine if there was a difference in rankings of more diverse departments. RESULTS: Hospitals with gastroenterology and GI surgery departments in the top 50 were more likely to mention diversity on their websites compared with hospitals that ranked from 51-100 (76% versus 56%; P = 0.035). The top 50 hospitals had a statistically significant higher percentage of underrepresented minority GI physicians and surgeons (7.01% versus 4.04%; P < 0.001). In the 31 states where these hospitals were located, there were more African Americans (13% versus 3%; P < 0.001) and Hispanics (12% versus 2%; P < 0.001), while there were fewer Asians (4% versus 21%; P < 0.001) in the population compared with the faculty. CONCLUSIONS: We used artificial intelligence software to determine the degree of racial and ethnic diversity in gastroenterology and GI surgery departments across the county. Higher ranking hospitals had a greater degree of diversity of their faculty and were more likely to emphasize diversity in their mission statements. Hospitals stress the importance of having a culturally diverse staff, yet their care providers may not adequately reflect the populations they serve. Further work is needed to prospectively track diversity rates over time and correlate these changes with measurable outcomes.

Automated Processing of Head CT Perfusion Imaging for Ischemic Stroke Triage: A Practical Guide to Quality Assurance and Interpretation.

Recent successful trials of thrombectomy launched a shift to imaging-based patient selection for stroke intervention. Many centers have adopted CT perfusion imaging (CTP) as a routine part of stroke workflow, and the demand for emergent CTP interpretation is growing. Fully automated CTP postprocessing software that rapidly generates standardized color-coded CTP summary maps with minimal user input and with easy accessibility of the software output is increasingly being adopted. Such automated postprocessing greatly streamlines clinical workflow and CTP interpretation for radiologists and other frontline physicians. However, the straightforward interface overshadows the computational complexity of the underlying postprocessing workflow, which, if not carefully examined, predisposes the interpreting physician to diagnostic errors. Using case examples, this article aims to familiarize the general radiologist with interpreting automated CTP software data output in the context of contemporary stroke management, providing a discussion of CTP acquisition and postprocessing, a stepwise guide for CTP quality assurance and troubleshooting, and a framework for avoiding clinically significant pitfalls of CTP interpretation in commonly encountered clinical scenarios. Interpreting radiologists should apply the outlined approach for quality assurance and develop a comprehensive search pattern for the identified pitfalls, to ensure accurate CTP interpretation and optimize patient selection for reperfusion.