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Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at the bedside. Given the complexity of the data ecosystem in large health systems, this task often represents a significant barrier to the real-world deployment of data science tools for prospective piloting and evaluation. Many information technology companies have created Machine Learning Operations (MLOps) teams to help with such tasks at scale, but the low penetration of home-grown data science tools in regular clinical practice precludes the formation of such teams in healthcare organizations. Based on experiences deploying data science tools at two large academic medical centers (Beth Israel Deaconess Medical Center, Boston, MA; Mayo Clinic, Rochester, MN), we propose a strategy to facilitate this transition from academic product to operational tool, defining the responsibilities of the principal investigator, data scientist, machine learning engineer, health system IT administrator, and clinician end-user throughout the process. We first enumerate the technical resources and stakeholders needed to prepare for model deployment. We then propose an approach to planning how the final product will work from data extraction and analysis to visualization of model outputs. Finally, we describe how the team should execute on this plan. We hope to guide health systems aiming to deploy minimum viable data science tools and realize their value in clinical practice.
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OBJECTIVES: Blood-based multi-cancer early detection (MCED) tests are now commercially available. However, there are currently no consensus guidelines available for head and neck cancer (HNC) providers to direct work up or surveillance for patients with a positive MCED test. We seek to describe cases of patients with positive MCED tests suggesting HNC and provide insights for their evaluation. METHODS: Retrospective chart review of patients referred to Otolaryngology with an MCED result suggesting HNC. Patients enrolled in prospective MCED clinical trials were excluded. Cancer diagnoses were confirmed via frozen-section pathology. RESULTS: Five patients were included (mean age: 69.2 years, range 50-87; 4 male) with MCED-identified-high-risk for HNC or lymphoma. Only patient was symptomatic. After physical exam and follow-up head and neck imaging, circulating tumor HPV DNA testing, two patients were diagnosed with p16 + oropharyngeal squamous cell carcinomas and underwent appropriate therapy. A third patient had no evidence of head and neck cancer but was diagnosed with sarcoma of the thigh. The remaining two patients had no evidence of malignancy after in-depth workup. CONCLUSIONS: In this retrospective study, 2 of 5 patients referred to Otolaryngology with a positive MCED result were diagnosed with HPV + oropharyngeal squamous cell carcinoma. We recommend that positive HNC MCED work up include thorough head and neck examination with flexible laryngoscopy and focused CT or MRI imaging. Given the potential for inaccurate MCED tissue of origin classification, PET/CT may be useful in specific situations. For a patient with no cancer identified, development of clear guidelines is warranted.
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Detección Precoz del Cáncer , Neoplasias de Cabeza y Cuello , Humanos , Masculino , Anciano , Persona de Mediana Edad , Femenino , Detección Precoz del Cáncer/métodos , Neoplasias de Cabeza y Cuello/diagnóstico , Neoplasias de Cabeza y Cuello/cirugía , Neoplasias de Cabeza y Cuello/patología , Anciano de 80 o más Años , Estudios Retrospectivos , Derivación y ConsultaRESUMEN
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.
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Estudio de Asociación del Genoma Completo , Polimorfismo de Nucleótido Simple , Predisposición Genética a la Enfermedad , Humanos , Aprendizaje Automático , Herencia MultifactorialRESUMEN
Electronic vaccine certificates (EVC) for COVID-19 vaccination are likely to become widespread. Blockchain (BC) is an electronic immutable distributed ledger and is one of the more common proposed EVC platform options. However, the principles of blockchain are not widely understood by public health and medical professionals. We attempt to describe, in an accessible style, how BC works and the potential benefits and drawbacks in its use for EVCs. Our assessment is BC technology is not well suited to be used for EVCs. Overall, blockchain technology is based on two key principles: the use of cryptography, and a distributed immutable ledger in the format of blockchains. While the use of cryptography can provide ease of sharing vaccination records while maintaining privacy, EVCs require some amount of contribution from a centralized authority to confirm vaccine status; this is partly because these authorities are responsible for the distribution and often the administration of the vaccine. Having the data distributed makes the role of a centralized authority less effective. We concluded there are alternative ways to use cryptography outside of a BC that allow a centralized authority to better participate, which seems necessary for an EVC platform to be of practical use.
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Objective: To assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs). Patients and Methods: We obtained data on all ED visits at our health care system's largest ED from May 5, 2018, to December 31, 2019. We also obtained data from 3 satellite EDs and 1 distant-hub ED from May 1, 2018, to December 31, 2018. A gradient-boosted machine model was trained on pooled data from the included EDs. To prevent the effect of differing training set sizes, the data were randomly downsampled to match those of our smallest ED. A second model was trained on this downsampled, pooled data. The model's performance was compared using area under the receiver operating characteristic (AUC). Finally, site-specific models were trained and tested across all the sites, and the importance of features was examined to understand the reasons for differing generalizability. Results: The training data sets contained 1918-64,161 ED visits. The AUC for the pooled model ranged from 0.84 to 0.94 across the sites; the performance decreased slightly when Ns were downsampled to match those of our smallest ED site. When site-specific models were trained and tested across all the sites, the AUCs ranged more widely from 0.71 to 0.93. Within a single ED site, the performance of the 5 site-specific models was most variable for our largest and smallest EDs. Finally, when the importance of features was examined, several features were common to all site-specific models; however, the weight of these features differed. Conclusion: A machine learning model for predicting hospital admission from the ED will generalize fairly well within the health care system but will still have significant differences in AUC performance across sites because of site-specific factors.
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OBJECTIVE: Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES. MATERIALS AND METHODS: This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES. RESULTS: Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2-Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria). DISCUSSION: Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias. CONCLUSION: The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.
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Inteligencia Artificial , Asma , Asma/diagnóstico , Sesgo , Niño , Atención a la Salud , Humanos , Aprendizaje Automático , Clase SocialRESUMEN
A majority of Americans favor universal health insurance, but there is uncertainty over how best to achieve this goal. Whatever the insurance design that is implemented, additional details that must be considered include breadth of services covered, restrictions and limits on volumes of services, cost-sharing for individuals, and pricing. In the hopes that research can inform this ongoing debate, we review evidence supporting different models for achieving universal coverage in the US and identify areas where additional research and stakeholder input is needed. Key areas in need of further research include how care should be organized, how costs can be reduced, and what healthcare services universal insurance should cover.
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Servicios de Salud , Cobertura Universal del Seguro de Salud , Accesibilidad a los Servicios de Salud , Humanos , Seguro de Salud , InvestigaciónRESUMEN
I.The Coronavirus Disease 2019 (COVID-19) has demonstrated that accurate forecasts of infection and mortality rates are essential for informing healthcare resource allocation, designing countermeasures, implementing public health policies, and increasing public awareness. However, there exist a multitude of modeling methodologies, and their relative performances in accurately forecasting pandemic dynamics are not currently comprehensively understood. In this paper, we introduce the non-mechanistic MIT-LCP forecasting model, and assess and compare its performance to various mechanistic and non-mechanistic models that have been proposed for forecasting COVID-19 dynamics. We performed a comprehensive experimental evaluation which covered the time period of November 2020 to April 2021, in order to determine the relative performances of MIT-LCP and seven other forecasting models from the United States' Centers for Disease Control and Prevention (CDC) Forecast Hub. Our results show that there exist forecasting scenarios well-suited to both mechanistic and non-mechanistic models, with mechanistic models being particularly performant for forecasts that are further in the future when recent data may not be as informative, and non-mechanistic models being more effective with shorter prediction horizons when recent representative data is available. Improving our understanding of which forecasting approaches are more reliable, and in which forecasting scenarios, can assist effective pandemic preparation and management.
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Hospital census prediction has well-described implications for efficient hospital resource utilization, and recent issues with hospital crowding due to CoVID-19 have emphasized the importance of this task. Our team has been leading an institutional effort to develop machine-learning models that can predict hospital census 12 hours into the future. We describe our efforts at developing accurate empirical models for this task. Ultimately, with limited resources and time, we were able to develop simple yet useful models for 12-hour census prediction and design a dashboard application to display this output to our hospital's decision-makers. Specifically, we found that linear models with ElasticNet regularization performed well for this task with relative 95% error of +/- 3.4% and that this work could be completed in approximately 7 months.
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Censos , Hospitales , COVID-19 , Humanos , Aprendizaje AutomáticoRESUMEN
OBJECTIVE: To estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination. DESIGN: Simulation agent based modeling study. SETTING: Simulated population based on real world US county. PARTICIPANTS: The simulation included 100 000 agents, with a representative distribution of demographics and occupations. Networks of contacts were established to simulate potentially infectious interactions though occupation, household, and random interactions. INTERVENTIONS: Simulation of standard covid-19 vaccination versus delayed second dose vaccination prioritizing the first dose. The simulation runs were replicated 10 times. Sensitivity analyses included first dose vaccine efficacy of 50%, 60%, 70%, 80%, and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread (that is, non-sterilizing vaccine); and an alternative vaccination strategy that implements delayed second dose for people under 65 years of age, but not until all those above this age have been vaccinated. MAIN OUTCOME MEASURES: Cumulative covid-19 mortality, cumulative SARS-CoV-2 infections, and cumulative hospital admissions due to covid-19 over 180 days. RESULTS: Over all simulation replications, the median cumulative mortality per 100 000 for standard dosing versus delayed second dose was 226 v 179, 233 v 207, and 235 v 236 for 90%, 80%, and 70% first dose efficacy, respectively. The delayed second dose strategy was optimal for vaccine efficacies at or above 80% and vaccination rates at or below 0.3% of the population per day, under both sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100 000. The delayed second dose strategy for people under 65 performed consistently well under all vaccination rates tested. CONCLUSIONS: A delayed second dose vaccination strategy, at least for people aged under 65, could result in reduced cumulative mortality under certain conditions.
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Vacunas contra la COVID-19/administración & dosificación , COVID-19/prevención & control , Salud Pública/estadística & datos numéricos , Tiempo de Tratamiento/estadística & datos numéricos , Vacuna nCoV-2019 mRNA-1273 , Adulto , Vacuna BNT162 , COVID-19/diagnóstico , COVID-19/epidemiología , COVID-19/virología , Vacunas contra la COVID-19/inmunología , Hospitalización , Humanos , Persona de Mediana Edad , Ocupaciones , Simulación de Paciente , SARS-CoV-2/genética , SARS-CoV-2/inmunología , Sensibilidad y Especificidad , Análisis de Sistemas , Resultado del Tratamiento , VacunaciónRESUMEN
OBJECTIVE: Electronic health record documentation by intensive care unit (ICU) clinicians may predict patient outcomes. However, it is unclear whether physician and nursing notes differ in their ability to predict short-term ICU prognosis. We aimed to investigate and compare the ability of physician and nursing notes, written in the first 48 hours of admission, to predict ICU length of stay and mortality using 3 analytical methods. MATERIALS AND METHODS: This was a retrospective cohort study with split sampling for model training and testing. We included patients ≥18 years of age admitted to the ICU at Beth Israel Deaconess Medical Center in Boston, Massachusetts, from 2008 to 2012. Physician or nursing notes generated within the first 48 hours of admission were used with standard machine learning methods to predict outcomes. RESULTS: For the primary outcome of composite score of ICU length of stay ≥7 days or in-hospital mortality, the gradient boosting model had better performance than the logistic regression and random forest models. Nursing and physician notes achieved area under the curves (AUCs) of 0.826 and 0.796, respectively, with even better predictive power when combined (AUC, 0.839). DISCUSSION: Models using only nursing notes more accurately predicted short-term prognosis than did models using only physician notes, but in combination, the models achieved the greatest accuracy in prediction. CONCLUSIONS: Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Physicians' and nurses' notes are both uniquely important in mortality prediction and combining these notes can produce a better predictive model.
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Cuidados Críticos , Unidades de Cuidados Intensivos , Humanos , Modelos Estadísticos , Pronóstico , Estudios RetrospectivosRESUMEN
OBJECTIVE: We aimed to develop a model for accurate prediction of general care inpatient deterioration. MATERIALS AND METHODS: Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call. RESULTS: Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score. DISCUSSION: Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering. CONCLUSIONS: MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.
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Puntuación de Alerta Temprana , Hospitalización , Humanos , Pacientes Internos , Unidades de Cuidados Intensivos , Aprendizaje AutomáticoRESUMEN
BACKGROUND: Burnout syndrome is very prevalent among healthcare residents. Initiatives addressing workload conditions have had limited impact on burnout. The present study aims to explore the contribution of two emotion regulation strategies, namely emotion suppression and cognitive reevaluation, to residents' burnout, while accounting for workload factors. METHODS: Participants were 105 residents (68.6% women; mean age = 27.5, SD = 3.0). They completed measures of workload, burnout, and emotion regulation. The study was cross-sectional. RESULTS: Emotional suppression was associated with higher burnout (depersonalization scale; ß = 0.20, p < 0.05, CI 0.15-2.48) and cognitive revaluation was linked to lower burnout (higher personal accomplishment; ß = 0.35, p < 0.01, CI 0.16-2.56), even after controlling for demographic and workload factors. We found interaction effects between workload variables (supervisor support and number of patient hours) and emotion regulation (p < 0.05). CONCLUSIONS: The relationship between workload, emotion regulation, and burnout seems to be complex. That is, similar work conditions might generate different levels of burnout depending on the resident's emotional regulation strategies. This might partly explain why existing initiatives based on workload changes have had a modest impact on burnout. Results also support including emotion regulation training in prevention and treatment programs targeting burnout during residency.
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Agotamiento Profesional , Regulación Emocional , Agotamiento Profesional/epidemiología , Agotamiento Psicológico , Estudios Transversales , Atención a la Salud , Femenino , Humanos , Masculino , Encuestas y Cuestionarios , Carga de TrabajoRESUMEN
BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. OBJECTIVE: The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. METHODS: A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. RESULTS: Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. CONCLUSION: We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.
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Sistemas de Apoyo a Decisiones Clínicas , Hospitales/estadística & datos numéricos , Aprendizaje Automático , Informática Médica/métodos , Readmisión del Paciente/estadística & datos numéricos , Humanos , Riesgo , Factores de TiempoRESUMEN
BACKGROUND: "Artificial intelligence" (AI) is often referred to as "augmented human intelligence" (AHI). The latter term implies that computers support-rather than replace-human decision-making. It is unclear whether the terminology used affects attitudes and perceptions in practice. METHODS: In the context of a quality improvement project implementing AI/AHI-based decision support in a regional health system, we surveyed staff's attitudes about AI/AHI, randomizing question prompts to refer to either AI or AHI. RESULTS: Ninety-three staff completed surveys. With a power of 0.95 to detect a difference larger than 0.8 points on a 5-point scale, we did not detect a significant difference in responses to six questions regarding attitudes when respondents were alternatively asked about AI versus AHI (mean difference range: 0.04-0.22 points; p > 0.05). CONCLUSION: Although findings may be setting-specific, we observed that use of the terms "AI" and "AHI" in a survey on attitudes of clinical staff elicited similar responses.
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Inteligencia Artificial , Actitud , Nombres , Humanos , Inteligencia , Encuestas y CuestionariosRESUMEN
: The need for multiple transducer positions, especially from right parasternal windows, is consistently mentioned in the recommendations for the accurate measurement of peak velocities across a stenotic aortic valve, but yet poorly adopted.We performed a subanalysis of the largest prospective series on the right parasternal acoustic windows in patients with aortic stenosis (330 consecutive) to calculate the degree of misalignment and estimate the potential outcome implication of this often-forgotten approach.The right parasternal view was highly feasible with an average estimated misalignment from the apical view of 14â±â16 degree; in 10 cases, an estimated misalignment >40 degree. Right parasternal assessment (vs. apical alone) provided a significant reclassification from moderate to severe or even very-severe aortic valve stenosis. Considering a wellestablished survival benefit provided by either percutaneous or surgical valve replacement in patients with severe aortic stenosis the reclassification would result in approximately 1 life-year saved for every 30-35 patients in whom parasternal view were effectively utilized.
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Estenosis de la Válvula Aórtica/diagnóstico por imagen , Válvula Aórtica/diagnóstico por imagen , Ecocardiografía Doppler , Válvula Aórtica/fisiopatología , Estenosis de la Válvula Aórtica/fisiopatología , Estenosis de la Válvula Aórtica/terapia , Toma de Decisiones Clínicas , Estudios de Factibilidad , Hemodinámica , Humanos , Posicionamiento del Paciente , Valor Predictivo de las Pruebas , Pronóstico , Índice de Severidad de la EnfermedadRESUMEN
PURPOSE: To identify and characterize studies evaluating clinician compliance with infection-related guidelines, and to explore trends in guideline design and implementation strategies. DATA SOURCES: PubMed database, April 2017. Followed the PRISMA Statement for systematic reviews. STUDY SELECTION: Scope was limited to studies reporting compliance with guidelines pertaining to the prevention, detection, and/or treatment of acute hospital-based infections. Initial search (1,499 titles) was reduced to 49 selected articles. DATA EXTRACTION: Extracted publication and guideline characteristics, outcome measures reported, and any results related to clinician compliance. Primary summary measures were frequencies and distributions of characteristics. Interventions that led to improved compliance results were analyzed to identify trends in guideline design and implementation. RESULTS OF DATA SYNTHESIS: Of the 49 selected studies, 18 (37%), 13 (27%), and 10 (20%) focused on sepsis, pneumonia, and general infection, respectively. Six (12%), 17 (35%), and 26 (53%) studies assessed local, national, and international guidelines, respectively. Twenty studies (41%) reported 1-instance compliance results, 28 studies (57%) reported 2-instance compliance results (either before-and-after studies or control group studies), and 1 study (2%) described compliance qualitatively. Average absolute change in compliance for minimal, decision support, and multimodal interventions was 10%, 14%, and 25%, respectively. Twelve studies (24%) reported no patient outcome alongside compliance. CONCLUSIONS: Multimodal interventions and quality improvement initiatives seem to produce the greatest improvement in compliance, but trends in other factors were inconsistent. Additional research is required to investigate these relationships and understand the implications behind various approaches to guideline design, communication, and implementation, in addition to effectiveness of protocol impact on relevant patient outcomes.
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Evaluación de Resultado en la Atención de Salud , Mejoramiento de la Calidad , Hospitales , HumanosRESUMEN
BACKGROUND: To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS). METHODS: A survey was designed to assess staff attitudes about AI-based CDS tools. The survey was anonymously and voluntarily completed by clinical staff in three primary care outpatient clinics before and after implementation of an AI-based CDS system aimed to improve glycemic control in patients with diabetes as part of a quality improvement project. The CDS identified patients at risk for poor glycemic control and generated intervention recommendations intended to reduce patients' risk. RESULTS: Staff completed 45 surveys pre-intervention and 38 post-intervention. Following implementation, staff felt that care was better coordinated (11 favorable responses, 14 unfavorable responses pre-intervention; 21 favorable responses, 3 unfavorable responses post-intervention; p < 0.01). However, only 14 % of users would recommend the AI-based CDS. Staff feedback revealed that the most favorable aspect of the CDS was that it promoted team dialog about patient needs (N = 14, 52 %), and the least favorable aspect was inadequacy of the interventions recommended by the CDS. CONCLUSIONS: AI-based CDS tools that are perceived negatively by staff may reduce staff excitement about AI technology, and hands-on experience with AI may lead to more realistic expectations about the technology's capabilities. In our setting, although AI-based CDS prompted an interdisciplinary discussion about the needs of patients at high risk for poor glycemic control, the interventions recommended by the CDS were often perceived to be poorly tailored, inappropriate, or not useful. Developers should carefully consider tasks that are best performed by AI and those best performed by the patient's care team.
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Inteligencia Artificial/estadística & datos numéricos , Sistemas de Apoyo a Decisiones Clínicas/organización & administración , Diabetes Mellitus/prevención & control , Personal de Salud/estadística & datos numéricos , Implementación de Plan de Salud , Médicos de Atención Primaria/psicología , Atención Primaria de Salud/organización & administración , Instituciones de Atención Ambulatoria , Inteligencia Artificial/tendencias , Índice Glucémico , Humanos , Mejoramiento de la Calidad , Programas Informáticos , Encuestas y CuestionariosRESUMEN
OBJECTIVE: The Dizziness Handicap Inventory (DHI) is a 25-item self-report questionnaire developed to measure the disabling and handicapping impact of dizziness. The present investigation was conducted in an effort to re-assess the factor structure of the DHI. STUDY DESIGN: Retrospective study. SETTING: Tertiary care center. PATIENTS: Subjects were 1,991 patients who were evaluated in the Mayo Clinic-Rochester Vestibular and Balance Laboratory. MAIN OUTCOME MEASURES: Exploratory factor analysis: an exploratory bifactor analysis (EFA) with bifactor rotation was used to analyze a random sample of 999 patients. Analyses were used to determine the dominance of the general factor (i.e., total score) relative to the group factor (i.e., subscales). Confirmatory factor analysis: a confirmatory bifactor graded response model was fit with appropriate item-to-group relationships that was discovered by our exploratory analyses. To validate the bifactor model that was identified with the exploratory analyses, a bifactor model with three grouping factors (i.e., Physical manifestations, Catastrophic impact of dizziness, and the Emotional impact of dizziness) were fit to a different random sample of 992 patients using the new item-to-group factor specifications. RESULTS: In the confirmatory analyses, all items had a positive factor loading on the general factor. There were 14 items that loaded on the general factor only. The rest of the items (nâ=â11) loaded on both the general factor and one of three group factors. CONCLUSIONS: Conclusions of the study revealed several findings: 1) reporting the result as a total score (i.e., a single general factor) is warranted, and, 2) there is statistical support for the existence of three subscales representing: the Physical manifestations, Catastrophic impact, and Emotional impact of dizziness and vertigo.
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Mareo/complicaciones , Autoinforme , Encuestas y Cuestionarios , Adulto , Anciano , Mareo/psicología , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios RetrospectivosRESUMEN
INTRODUCTION: Identification of hospitalized patients with suddenly unfavorable clinical course remains challenging. Models using objective data elements from the electronic health record may miss important sources of information available to nurses. METHODS: We recorded nurses' perception of patient potential for deterioration in 2 medical and 2 surgical adult hospital units using a 5-point score at the start of the shift (the Worry Factor [WF]), and any time a change or an increase was noted by the nurse. Cases were evaluated by three reviewers. Intensive care unit (ICU) transfers were also tracked. RESULTS: 31 159 patient-shifts were recorded for 3185 unique patients during 3551 hospitalizations, with 169 total outcome events. Out of 492 potential deterioration events identified, 380 (77%) were confirmed by reviewers as true deterioration events. Likelihood ratios for ICU transfer were 17.8 (15.2-20.9) in the 24 hours following a WF > 2, and 40.4 (27.1-60.1) following a WF > 3. Accuracy rates were significantly higher in nurses with over a year of experience (68% vs 79%, P = 0.04). The area under the receiver operator characteristic curve (AUROC) was 0.92 for the prediction of ICU transfer within 24 hours. DISCUSSION: This is a higher accuracy than most published early warning scores. CONCLUSION: Nurses' pattern recognition and sense of worry can provide important information for the detection of acute physiological deterioration and should be included in the electronic medical record.