The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study.

BACKGROUND: Sensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors driving the potential utility of classification models as decision support tools for predicting readmissions in patients with HF. OBJECTIVE: The primary objective of this study is to assess the trade-off between using deep learning (DL) and traditional ML models to identify the risk of 100-day readmissions in patients with HF. Additionally, the study aims to provide explanations for the model predictions by highlighting important features both on a global scale across the patient cohort and on a local level for individual patients. METHODS: The retrospective data for this study were obtained from the Regional Health Care Information Platform in Region Halland, Sweden. The study cohort consisted of patients diagnosed with HF who were over 40 years old and had been hospitalized at least once between 2017 and 2019. Data analysis encompassed the period from January 1, 2017, to December 31, 2019. Two ML models were developed and validated to predict 100-day readmissions, with a focus on the explainability of the model's decisions. These models were built based on decision trees and recurrent neural architecture. Model explainability was obtained using an ML explainer. The predictive performance of these models was compared against 2 risk assessment tools using multiple performance metrics. RESULTS: The retrospective data set included a total of 15,612 admissions, and within these admissions, readmission occurred in 5597 cases, representing a readmission rate of 35.85%. It is noteworthy that a traditional and explainable model, informed by clinical knowledge, exhibited performance comparable to the DL model and surpassed conventional scoring methods in predicting readmission among patients with HF. The evaluation of predictive model performance was based on commonly used metrics, with an area under the precision-recall curve of 66% for the deep model and 68% for the traditional model on the holdout data set. Importantly, the explanations provided by the traditional model offer actionable insights that have the potential to enhance care planning. CONCLUSIONS: This study found that a widely used deep prediction model did not outperform an explainable ML model when predicting readmissions among patients with HF. The results suggest that model transparency does not necessarily compromise performance, which could facilitate the clinical adoption of such models.

Characteristics, management and outcomes in patients with CKD in a healthcare region in Sweden: a population-based, observational study.

OBJECTIVES: To describe chronic kidney disease (CKD) regarding treatment rates, comorbidities, usage of CKD International Classification of Diseases (ICD) diagnosis, mortality, hospitalisation, evaluate healthcare utilisation and screening for CKD in relation to new nationwide CKD guidelines. DESIGN: Population-based observational study. SETTING: Healthcare registry data of patients in Southwest Sweden. PARTICIPANTS: A total cohort of 65 959 individuals aged >18 years of which 20 488 met the criteria for CKD (cohort 1) and 45 470 at risk of CKD (cohort 2). PRIMARY AND SECONDARY OUTCOME MEASURES: Data were analysed with regards to prevalence, screening rates of blood pressure, glucose, estimated glomerular filtration rate (eGFR), Urinary-albumin-creatinine ratio (UACR) and usage of ICD-codes for CKD. Mortality and hospitalisation were analysed with logistic regression models. RESULTS: Of the CKD cohort, 18% had CKD ICD-diagnosis and were followed annually for blood pressure (79%), glucose testing (76%), eGFR (65%), UACR (24%). UACR follow-up was two times as common in hypertensive and cardiovascular versus diabetes patients with CKD with a similar pattern in those at risk of CKD. Statin and renin-angiotensin-aldosterone inhibitor appeared in 34% and 43%, respectively. Mortality OR at CKD stage 5 was 1.23 (CI 0.68 to 0.87), diabetes 1.20 (CI 1.04 to 1.38), hypertension 1.63 (CI 1.42 to 1.88), atherosclerotic cardiovascular disease (ASCVD) 1.84 (CI 1.62 to 2.09) associated with highest mortality risk. Hospitalisation OR in CKD stage 5 was 1.96 (CI 1.40 to 2.76), diabetes 1.15 (CI 1.06 to 1.25), hypertension 1.23 (CI 1.13 to 1.33) and ASCVD 1.52 (CI 1.41 to 1.64). CONCLUSIONS: The gap between patients with CKD by definition versus those diagnosed as such was large. Compared with recommendations patients with CKD have suboptimal follow-up and treatment with renin-angiotensin-aldosterone system inhibitor and statins. Hypertension, diabetes and ASCVD were associated with increased mortality and hospitalisation. Improved screening and diagnosis of CKD, identification and management of risk factors and kidney protective treatment could affect clinical and economic outcomes.

Deep learning prediction models based on EHR trajectories: A systematic review.

BACKGROUND: Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' future health-related risks. It enables healthcare systems to increase the quality of care through early identification and primary prevention. Deep learning techniques have shown great capacity for analyzing complex data and have been successful for prediction tasks using complex EHR trajectories. This systematic review aims to analyze recent studies to identify challenges, knowledge gaps, and ongoing research directions. METHODS: For this systematic review, we searched Scopus, PubMed, IEEE Xplore, and ACM databases from Jan 2016 to April 2022 using search terms centered around EHR, deep learning, and trajectories. Then the selected papers were analyzed according to publication characteristics, objectives, and their solutions regarding existing challenges, such as the model's capacity to deal with intricate data dependencies, data insufficiency, and explainability. RESULTS: After removing duplicates and out-of-scope papers, 63 papers were selected, which showed rapid growth in the number of research in recent years. Predicting all diseases in the next visit and the onset of cardiovascular diseases were the most common targets. Different contextual and non-contextual representation learning methods are employed to retrieve important information from the sequence of EHR trajectories. Recurrent neural networks and the time-aware attention mechanism for modeling long-term dependencies, self-attentions, convolutional neural networks, graphs for representing inner visit relations, and attention scores for explainability were frequently used among the reviewed publications. CONCLUSIONS: This systematic review demonstrated how recent breakthroughs in deep learning methods have facilitated the modeling of EHR trajectories. Research on improving the ability of graph neural networks, attention mechanisms, and cross-modal learning to analyze intricate dependencies among EHRs has shown good progress. There is a need to increase the number of publicly available EHR trajectory datasets to allow for easier comparison among different models. Also, very few developed models can handle all aspects of EHR trajectory data.

Domain Knowledge-Driven Generation of Synthetic Healthcare Data.

Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing artificial intelligence algorithms. However, access to "real" healthcare data is a big challenge due to ethical and legal reasons. There is also a need to deal with challenges around electronic health records (EHRs) including biased, heterogeneity, imbalanced data, and small sample sizes. In this study, we introduce a domain knowledge-driven framework for generating synthetic EHRs, as an alternative to methods only using EHR data or expert knowledge. By leveraging external medical knowledge sources in the training algorithm, the suggested framework is designed to maintain data utility, fidelity, and clinical validity while preserving patient privacy.

A Framework for Evaluating Synthetic Electronic Health Records.

Synthetic data generation can be applied to Electronic Health Records (EHRs) to obtain synthetic versions that do not compromise patients' privacy. However, the proliferation of synthetic data generation techniques has led to the introduction of a wide variety of methods for evaluating the quality of generated data. This makes the task of evaluating generated data from different models challenging as there is no consensus on the methods used. Hence the need for standard ways of evaluating the generated data. In addition, the available methods do not assess whether dependencies between different variables are maintained in the synthetic data. Furthermore, synthetic time series EHRs (patient encounters) are not well investigated, as the available methods do not consider the temporality of patient encounters. In this work, we present an overview of evaluation methods and propose an evaluation framework to guide the evaluation of synthetic EHRs.

Interdisciplinary Human-Centered AI for Hospital Readmission Prediction of Heart Failure Patients.

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages.

A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning.

Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes).

Temporal Context Matters: An Explainable Model for Medical Resource Utilization in Chronic Kidney Disease.

The prediction of medical resource utilization is beneficial for effective healthcare resource planning and allocation. Previous work in resource utilization prediction can be categorized into two main classes, count-based and trajectory-based. Both of these classes have some challenges, in this work we propose a hybrid approach to overcome these challenges. Our initial results promote the value of temporal context in resource utilization prediction and highlight the importance of model explainability in understanding the main important variables.

Clinical characteristics at hospital discharge that predict cardiovascular readmission within 100 days in heart failure patients - An observational study.

Background: After a heart failure (HF) hospital discharge, the risk of a cardiovascular (CV) related event is highest in the following 100 days. It is important to identify factors associated with increased risk of readmission. Method: This retrospective, population-based study examined HF patients in Region Halland (RH), Sweden, hospitalized with a HF diagnosis between 2017 and 2019. Data regarding patient clinical characteristics were retrieved from the Regional healthcare Information Platform from admission until 100 days post-discharge. Primary outcome was readmission due to a CV related event within 100 days. Results: There were 5029 included patients being admitted for HF and discharged and 1966 (39%) were newly diagnosed. Echocardiography was available for 3034 (60%) patients and 1644 (33%) had their first echocardiography while admitted. The distribution of HF-phenotypes was 33% HF with reduced ejection fraction (EF), 29% HF with mildly reduced EF and 38% HF with preserved EF. Within 100 days, 1586 (33%) patients were readmitted, and 614 (12%) died. A Cox regression model showed that advanced age, longer hospital length of stay, renal impairment, high heart rate and elevated NT-proBNP were associated with an increased risk of readmission regardless of HF-phenotype. Women and increased blood pressure are associated with a reduced risk of readmission. Conclusions: One third had a CV-readmission within 100 days. This study found clinical factors already present at discharge that are associated with increased risk of readmission which should be considered at discharge.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. MATERIALS AND METHODS: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. RESULTS: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. CONCLUSION: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.

Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study.

BACKGROUND: There is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension. OBJECTIVE: The overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension. METHODS: The study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study. RESULTS: The focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it. CONCLUSIONS: We hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff. TRIAL REGISTRATION: ClinicalTrials.gov NCT04413500; https://clinicaltrials.gov/ct2/show/NCT04413500. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24494.

Towards a Knowledge Graph-Based Explainable Decision Support System in Healthcare.

The decisions derived from AI-based clinical decision support systems should be explainable and transparent so that the healthcare professionals can understand the rationale behind the predictions. To improve the explanations, knowledge graphs are a well-suited choice to be integrated into eXplainable AI. In this paper, we introduce a knowledge graph-based explainable framework for AI-based clinical decision support systems to increase their level of explainability.

Measurement Model of Women's Preferences in Obstetrician and Gynecologist Selection in the Private Sector: Exploratory and Confirmatory Factor Analysis.

BACKGROUND: The purpose of this study is to construct and validate a measurement model of women's preferences in Obstetrician and Gynecologist (OB/GYN) selection in the private sector of non-clinical parameters. METHODS: This methodological study included 462 respondents in OB/GYN's offices to a researcher-made questionnaire. The patients visited 57 offices of OB/GYNs in the city of Mashhad in Iran and completed women's preferences in OB/GYN selection questionnaire over a 2-month period from January to February 2018. Exploratory Factor Analysis (EFA) was conducted to verify the instrument's construct validity. Confirmatory Factor Analysis (CFA) was used to test whether the data fit our hypothesized model obtained from EFA model. RESULTS: The first draft of the questionnaire was prepared with 118 items based on literature review. The outcome of content validity assessment was a 51-item questionnaire. Scale-Content Validity Index (S-CVI) turned out to be 0.80. The results of EFA yielded an instrument with 33 items in six domains, which explained 52.657% of the total variance of the questionnaire. With performing CFA, the 6-factor model with 29 items demonstrated a good fit with the data (CFI=0.952, CMIN/DF=1.613, RMSEA=0.036). Availability and Accessibility, Communicational Skills, Office Environment, Recommendation by Others, Special Services, and Cost and Insurance were found to define the women's preferences in OB/GYN selection in private sector, Iran. CONCLUSION: The developed measurement model considers the patient's preferences that influence decision-making process on OB/GYN selection. It can provide useful knowledge for OB/GYNs and policymakers to design appropriate and efficient marketing strategies according to the consumer preferences priority.

How Behavior Change Strategies are Used to Design Digital Interventions to Improve Medication Adherence and Blood Pressure Among Patients With Hypertension: Systematic Review.

BACKGROUND: Information on how behavior change strategies have been used to design digital interventions (DIs) to improve blood pressure (BP) control or medication adherence (MA) for patients with hypertension is currently limited. OBJECTIVE: Hypertension is a major modifiable risk factor for cardiovascular diseases and can be controlled with appropriate medication. Many interventions that target MA to improve BP are increasingly using modern digital technologies. This systematic review was conducted to discover how DIs have been designed to improve MA and BP control among patients with hypertension in the recent 10 years. Results were mapped into a matrix of change objectives using the Intervention Mapping framework to guide future development of technologies to improve MA and BP control. METHODS: We included all the studies regarding DI development to improve MA or BP control for patients with hypertension published in PubMed from 2008 to 2018. All the DI components were mapped into a matrix of change objectives using the Intervention Mapping technique by eliciting the key determinant factors (from patient and health care team and system levels) and targeted patient behaviors. RESULTS: The analysis included 54 eligible studies. The determinants were considered at two levels: patient and health care team and system. The most commonly described determinants at the patient level were lack of education, lack of self-awareness, lack of self-efficacy, and forgetfulness. Clinical inertia and an inadequate health workforce were the most commonly targeted determinants at the health care team and system level. Taking medication, interactive patient-provider communication, self-measurement, and lifestyle management were the most cited patient behaviors at both levels. Most of the DIs did not include support from peers or family members, despite its reported effectiveness and the rate of social media penetration. CONCLUSIONS: This review highlights the need to design a multifaceted DI that can be personalized according to patient behavior(s) that need to be changed to overcome the key determinant(s) of low adherence to medication or uncontrolled BP among patients with hypertension, considering different levels including patient and healthcare team and system involvement.

Concept based auto-assignment of healthcare questions to domain experts in online Q&A communities.

BACKGROUND: Healthcare consumers are increasingly turning to the online health Q&A communities to seek answers for their questions because current general search engines are unable to digest complex health-related questions. Q&A communities are platforms where users ask unstructured questions from different healthcare topics. OBJECTIVES: This study aimed to provide a concept-based approach to automatically assign health questions to the appropriate domain experts. METHODS: We developed three processes for (1) expert profiling, (2) question analysis and (3) similarity calculation and assignment. Semantic weight of concepts combined with TF-IDF weighting comprised vectors of concepts as expert profiles. Subsequently, the similarity between submitted questions and expert profiles was calculated to find a relevant expert. RESULTS: We randomly selected 345 questions posted by consumers for 38 experts in 13 health topics from NetWellness as input data. Our results showed the precision and recall of our proposed method for the studied topics were between 63 %-92 % and 61 %-100 %, respectively. The calculated F-measure in selected topics was between 62 % (Addiction and Substance Abuse) and 94 % (Eye and Vision Care) with a combined F-measure of 80 %. CONCLUSIONS: Concept-based methods using unified medical language system and natural language processing techniques could automatically assign actual health questions in different topics to the relevant domain experts with good performance metrics.

Similarity of medical concepts in question and answering of health communities.

The ability to automatically categorize submitted questions based on topics and suggest similar question and answer to the users reduces the number of redundant questions. Our objective was to compare intra-topic and inter-topic similarity between question and answers by using concept-based similarity computing analysis. We gathered existing question and answers from several popular online health communities. Then, Unified Medical Language System concepts related to selected questions and experts in different topics were extracted and weighted by term frequency -inverse document frequency values. Finally, the similarity between weighted vectors of Unified Medical Language System concepts was computed. Our result showed a considerable gap between intra-topic and inter-topic similarities in such a way that the average of intra-topic similarity (0.095, 0.192, and 0.110, respectively) was higher than the average of inter-topic similarity (0.012, 0.025, and 0.018, respectively) for questions of the top 3 popular online communities including NetWellness, WebMD, and Yahoo Answers. Similarity scores between the content of questions answered by experts in the same and different topics were calculated as 0.51 and 0.11, respectively. Concept-based similarity computing methods can be used in developing intelligent question and answering retrieval systems that contain auto recommendation functionality for similar questions and experts.

Design and Evaluation of a Smart Medication Recommendation System for the Electronic Prescription.

BACKGROUND: electronic prescription is shown to have many benefits in terms of reducing medication errors, improving patient safety, productivity, and resource management, but it may cause new errors and physician frustration if not designed and implemented properly. Improving usability and user-centered design is essential for physicians' adoption. OBJECTIVES: To enhance the efficiency of the e-prescribing system by reducing the risk of inappropriate selection of the medication and also to reduce the prescribing time and effort to reach the desired drug. METHODS: Important data fields for predicting medications were determined through interviews with pharmacists. Among those, fields which were available in a claims dataset of 16 million prescriptions were extracted and were used to develop a neural network model to be used by a recommender system that displays the most probable medications on top of the drop-down list in the e-prescription application. RESULTS: Offline and field evaluations both showed that this model could improve performance. CONCLUSION: smart recommenders systems can improve e-prescription usability, safety, and enhanced physicians' adoption.

Quantitative Evaluation of Diabetic Medical Record Documentation.

BACKGROUND: The documentation of medical records of diabetic patients is very important for the treatment of diabetes. The purpose of this study was to conduct quantitative evaluations of the Diabetic Medical Record (DMR) and Documentation Completeness Rate (DCR). METHODS: In this retrospective study, we evaluated the DCR of DMRs in the Comprehensive Diabetes Center of Imam Reza Hospital (CDRIRH). A checklist was prepared to evaluate the DCR. The overall assessment of the DCR was represented according to the following rating: 95-100% as strong, 75-94% as moderate, and less than 75% as weak. The free texts that physicians recorded in the DMRs were extracted to identify the data elements that physicians must record. In addition, the clinical importance of the data elements of the DMRs from the perspective of the endocrinologists was determined and then compared with the DCR. RESULTS: In this study, 1,200 DMRs and DCRs for 50 data elements in eight major categories were evaluated. The total DCR average was 30% and data elements in the laboratory test results category demonstrated the highest DCR (50.5%), whereas the least percentage was demonstrated in the internal visits category. The DCR for the other main categories was: demographic information = 48.5%; patient referral information = 14.2%; diagnosis = 5%; anti-hyperglycemic medications = 25.5%; diabetic complications = 17.7%; and results of specialty and subspecialty consultation = 41.7%. The evaluation of the free text data element in the DMRs indicated that physicians documented free text data elements in three categories. CONCLUSION: Our results demonstrated a weak level of documentation in the DMRs. The physicians had written many data elements in the margins of the DMRs. Therefore, it indicates the necessity to modify and change the structure of the DMR.

Clinical Dashboard in the Intensive Care Unit: Need-Assessment and Survey about Attitudes and Acceptance of Tele-ICU from the Viewpoint of Nurses and Clinicians in the Intensive Care Unit.

BACKGROUND: One of the most worrying aspects of medical area in developing countries is the Intensive Care Unit (ICU). This study aimed to evaluate the acceptability of the clinical dashboard by the users, prior to final use and their attitude towards this technology, as well as to examine the specific needs that Tele-ICU technology can cover in the form of a clinical dashboard. MATERIALS AND METHODS: This study was conducted at Shahid Bahonar Hospital of Kerman, Southeastern Iran, with three ICUs, the first, second, and third sections of which had 10, 12, and 24 beds, respectively. Taking survey and need assessment of care providers, qualitative and quantitative analyses were undertaken to identify key positive and negative themes. The data were analyzed by SPSS software version 18. RESULTS: About 82% of care providers in the ICU participated in this survey. The number of participants based on the groups in the survey was 98 (81.7%) of the nurses and respiratory therapists group, 20 (80%) from the group of anesthesiologists and 20 (87%) from the group of anesthesiologist assistants who participated in the survey. About 51% of the survey participants completed the description section either partially or totally. On average, among all groups, the group of anesthesiologists had the most and the nurses had the least knowledge about telemedicine and Tele-ICU, whereas the anesthesiologist assistants had the most and the nurses and respiratory therapists group had the least knowledge about clinical dashboards. CONCLUSION: This study showed that the level of knowledge and awareness of care providers, especially nurses and respiratory therapists in the ICU in terms of telemedicine and Tele-ICU is low and care providers are in doubt that telemedicine technology could have a positive or negative impact on human resource shortages, yet agreed that it would have a negative effect on the privacy of the patients and care providers. In addition, the ICU care providers agree that Tele-ICU can positively affect the quality of patient care, staff satisfaction, reduce the cost of care, and ease and reduce the time for patient counseling. This suggests the need for further research and education of system impact beyond patient outcomes related to this new technology.