Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 75
Filter
1.
JMIR Mhealth Uhealth ; 10(9): e38364, 2022 09 19.
Article in English | MEDLINE | ID: covidwho-2054780

ABSTRACT

BACKGROUND: Symptom checkers are clinical decision support apps for patients, used by tens of millions of people annually. They are designed to provide diagnostic and triage advice and assist users in seeking the appropriate level of care. Little evidence is available regarding their diagnostic and triage accuracy with direct use by patients for urgent conditions. OBJECTIVE: The aim of this study is to determine the diagnostic and triage accuracy and usability of a symptom checker in use by patients presenting to an emergency department (ED). METHODS: We recruited a convenience sample of English-speaking patients presenting for care in an urban ED. Each consenting patient used a leading symptom checker from Ada Health before the ED evaluation. Diagnostic accuracy was evaluated by comparing the symptom checker's diagnoses and those of 3 independent emergency physicians viewing the patient-entered symptom data, with the final diagnoses from the ED evaluation. The Ada diagnoses and triage were also critiqued by the independent physicians. The patients completed a usability survey based on the Technology Acceptance Model. RESULTS: A total of 40 (80%) of the 50 participants approached completed the symptom checker assessment and usability survey. Their mean age was 39.3 (SD 15.9; range 18-76) years, and they were 65% (26/40) female, 68% (27/40) White, 48% (19/40) Hispanic or Latino, and 13% (5/40) Black or African American. Some cases had missing data or a lack of a clear ED diagnosis; 75% (30/40) were included in the analysis of diagnosis, and 93% (37/40) for triage. The sensitivity for at least one of the final ED diagnoses by Ada (based on its top 5 diagnoses) was 70% (95% CI 54%-86%), close to the mean sensitivity for the 3 physicians (on their top 3 diagnoses) of 68.9%. The physicians rated the Ada triage decisions as 62% (23/37) fully agree and 24% (9/37) safe but too cautious. It was rated as unsafe and too risky in 22% (8/37) of cases by at least one physician, in 14% (5/37) of cases by at least two physicians, and in 5% (2/37) of cases by all 3 physicians. Usability was rated highly; participants agreed or strongly agreed with the 7 Technology Acceptance Model usability questions with a mean score of 84.6%, although "satisfaction" and "enjoyment" were rated low. CONCLUSIONS: This study provides preliminary evidence that a symptom checker can provide acceptable usability and diagnostic accuracy for patients with various urgent conditions. A total of 14% (5/37) of symptom checker triage recommendations were deemed unsafe and too risky by at least two physicians based on the symptoms recorded, similar to the results of studies on telephone and nurse triage. Larger studies are needed of diagnosis and triage performance with direct patient use in different clinical environments.


Subject(s)
Decision Support Systems, Clinical , Emergency Service, Hospital , Physicians , Adolescent , Adult , Aged , Emergency Service, Hospital/organization & administration , Female , Humans , Middle Aged , Surveys and Questionnaires , Triage/methods , Young Adult
2.
J Med Internet Res ; 24(9): e37900, 2022 09 30.
Article in English | MEDLINE | ID: covidwho-2054774

ABSTRACT

BACKGROUND: People who smoke have other risk factors for chronic diseases, such as low levels of physical activity and poor diet. Clinical decision support systems (CDSSs) might help health care practitioners integrate interventions for diet and physical activity into their smoking cessation programming but could worsen quit rates. OBJECTIVE: The aims of this study are to assess the effects of the addition of a CDSS for physical activity and diet on smoking cessation outcomes and to assess the implementation of the study. METHODS: We conducted a pragmatic hybrid type I effectiveness-implementation trial with 232 team-based primary care practices in Ontario, Canada, from November 2019 to May 2021. We used a 2-arm randomized controlled trial comparing a CDSS addressing physical activity and diet to treatment as usual and used the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework to measure implementation outcomes. The primary outcome was self-reported 7-day tobacco abstinence at 6 months. RESULTS: We enrolled 5331 participants in the study. Of these, 2732 (51.2%) were randomized to the intervention group and 2599 (48.8%) to the control group. At the 6-month follow-up, 29.7% (634/2137) of respondents in the intervention arm and 27.3% (552/2020) in the control arm reported abstinence from tobacco. After multiple imputation, the absolute group difference was 2.1% (95% CI -0.5 to 4.6; F1,1000.42=2.43; P=.12). Mean exercise minutes changed from 32 (SD 44.7) to 110 (SD 196.1) in the intervention arm and from 32 (SD 45.1) to 113 (SD 195.1) in the control arm (group effect: B=-3.7 minutes; 95% CI -17.8 to 10.4; P=.61). Servings of fruit and vegetables changed from 2.64 servings to 2.42 servings in the intervention group and from 2.52 servings to 2.45 servings in the control group (incidence rate ratio for intervention group=0.98; 95% CI 0.93-1.02; P=.35). CONCLUSIONS: A CDSS for physical activity and diet may be added to a smoking cessation program without affecting the outcomes. Further research is needed to improve the impact of integrated health promotion interventions in primary care smoking cessation programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04223336 https://www.clinicaltrials.gov/ct2/show/NCT04223336. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19157.


Subject(s)
Decision Support Systems, Clinical , Smoking Cessation , Delivery of Health Care , Diet, Healthy , Exercise , Humans , Ontario
3.
BMC Med Inform Decis Mak ; 22(1): 217, 2022 08 13.
Article in English | MEDLINE | ID: covidwho-2002167

ABSTRACT

BACKGROUND: Primary care providers face challenges in recognizing and controlling hypertension in patients with chronic kidney disease (CKD). Clinical decision support (CDS) has the potential to aid clinicians in identifying patients who could benefit from medication changes. This study designed an alert to control hypertension in CKD patients using an iterative human-centered design process. METHODS: In this study, we present a human-centered design process employing multiple methods for gathering user requirements and feedback on design and usability. Initially, we conducted contextual inquiry sessions to gather user requirements for the CDS. This was followed by group design sessions and one-on-one formative think-aloud sessions to validate requirements, obtain feedback on the design and layout, uncover usability issues, and validate changes. RESULTS: This study included 20 participants. The contextual inquiry produced 10 user requirements which influenced the initial alert design. The group design sessions revealed issues related to several themes, including recommendations and clinical content that did not match providers' expectations and extraneous information on the alerts that did not provide value. Findings from the individual think-aloud sessions revealed that participants disagreed with some recommended clinical actions, requested additional information, and had concerns about the placement in their workflow. Following each step, iterative changes were made to the alert content and design. DISCUSSION: This study showed that participation from users throughout the design process can lead to a better understanding of user requirements and optimal design, even within the constraints of an EHR alerting system. While raising awareness of design needs, it also revealed concerns related to workflow, understandability, and relevance. CONCLUSION: The human-centered design framework using multiple methods for CDS development informed the creation of an alert to assist in the treatment and recognition of hypertension in patients with CKD.


Subject(s)
Decision Support Systems, Clinical , Hypertension , Renal Insufficiency, Chronic , Feedback , Humans , Hypertension/complications , Hypertension/therapy , Renal Insufficiency, Chronic/complications , Renal Insufficiency, Chronic/therapy , Workflow
4.
Stud Health Technol Inform ; 290: 479-483, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1933565

ABSTRACT

The global COVID-19 pandemic has driven innovations in methods to sustain initiatives for the design, development, evaluation, and implementation of clinical support technology in long-term care settings while removing risk of infection for residents, family members, health care workers, researchers and technical professionals. We adapted traditional design and evaluation methodology for a mobile clinical decision support app - designated Mobile Application Information System for Integrated Evidence ("MAISIE") - to a completely digital design methodology that removes in-person contacts between the research team, developer, and nursing home staff and residents. We have successfully maintained project continuity for MAISIE app development with only minor challenges while working remotely. This digital design methodology can be implemented in projects where software can be installed without in-person technical support and remote work is feasible. Team skills, experience, and relationships are key considerations for adapting to digital environments and maintaining project momentum.


Subject(s)
COVID-19 , Decision Support Systems, Clinical , Mobile Applications , Health Personnel , Humans , Long-Term Care , Pandemics
5.
BMJ ; 377: e069271, 2022 06 27.
Article in English | MEDLINE | ID: covidwho-1909708

ABSTRACT

OBJECTIVE: To determine the effect of a user centered clinical decision support tool versus usual care on rates of initiation of buprenorphine in the routine emergency care of individuals with opioid use disorder. DESIGN: Pragmatic cluster randomized controlled trial (EMBED). SETTING: 18 emergency department clusters across five healthcare systems in five states representing the north east, south east, and western regions of the US, ranging from community hospitals to tertiary care centers, using either the Epic or Cerner electronic health record platform. PARTICIPANTS: 599 attending emergency physicians caring for 5047 adult patients presenting with opioid use disorder. INTERVENTION: A user centered, physician facing clinical decision support system seamlessly integrated into user workflows in the electronic health record to support initiating buprenorphine in the emergency department by helping clinicians to diagnose opioid use disorder, assess the severity of withdrawal, motivate patients to accept treatment, and complete electronic health record tasks by automating clinical and after visit documentation, order entry, prescribing, and referral. MAIN OUTCOME MEASURES: Rate of initiation of buprenorphine (administration or prescription of buprenorphine) in the emergency department among patients with opioid use disorder. Secondary implementation outcomes were measured with the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework. RESULTS: 1 413 693 visits to the emergency department (775 873 in the intervention arm and 637 820 in the usual care arm) from November 2019 to May 2021 were assessed for eligibility, resulting in 5047 patients with opioid use disorder (2787 intervention arm, 2260 usual care arm) under the care of 599 attending physicians (340 intervention arm, 259 usual care arm) for analysis. Buprenorphine was initiated in 347 (12.5%) patients in the intervention arm and in 271 (12.0%) patients in the usual care arm (adjusted generalized estimating equations odds ratio 1.22, 95% confidence interval 0.61 to 2.43, P=0.58). Buprenorphine was initiated at least once by 151 (44.4%) physicians in the intervention arm and by 88 (34.0%) in the usual care arm (1.83, 1.16 to 2.89, P=0.01). CONCLUSIONS: User centered clinical decision support did not increase patient level rates of initiating buprenorphine in the emergency department. Although streamlining and automating electronic health record workflows can potentially increase adoption of complex, unfamiliar evidence based practices, more interventions are needed to look at other barriers to the treatment of addiction and increase the rate of initiating buprenorphine in the emergency department in patients with opioid use disorder. TRIAL REGISTRATION: ClinicalTrials.gov NCT03658642.


Subject(s)
Buprenorphine , Decision Support Systems, Clinical , Opioid-Related Disorders , Adult , Buprenorphine/therapeutic use , Emergency Service, Hospital , Humans , Narcotic Antagonists/therapeutic use , Opiate Substitution Treatment/methods , Opioid-Related Disorders/drug therapy
6.
J Clin Anesth ; 80: 110877, 2022 09.
Article in English | MEDLINE | ID: covidwho-1878228

ABSTRACT

STUDY OBJECTIVE: We explored the feasibility of a Clinical Decision Support System (CDSS) to guide evidence-based perioperative anticoagulation. DESIGN: Prospective randomised clinical management simulation multicentre study. SETTING: Five University and 11 general hospitals in Germany. PARTICIPANTS: We enrolled physicians (anaesthesiologist (n = 73), trauma surgeons (n = 2), unknown (n = 1)) with different professional experience. INTERVENTIONS: A CDSS based on a multiple-choice test was developed and validated at the University Hospital of Frankfurt (phase-I). The CDSS comprised European guidelines for the management of anticoagulation in cardiology, cardio-thoracic, non-cardio-thoracic surgery and anaesthesiology. Phase-II compared the efficiency of physicians in identifying evidence-based approach of managing perioperative anticoagulation. In total 168 physicians were randomised to CDSS (PERI-KOAG) or CONTROL. MEASUREMENTS: Overall mean score and association of processing time and professional experience were analysed. The multiple-choice test consists of 11 cases and two correct answers per question were required to gain 100% success rate (=22 points). MAIN RESULTS: In total 76 physicians completed the questionnaire (n = 42 PERI-KOAG; n = 34 CONTROL; attrition rate 54%). Overall mean score (max. 100% = 22 points) was significantly higher in PERI-KOAG compared to CONTROL (82 ± 15% vs. 70 ± 10%; 18 ± 3 vs. 15 ± 2 points; P = 0.0003). A longer processing time is associated with significantly increased overall mean scores in PERI-KOAG (≥33 min. 89 ± 10% (20 ± 2 points) vs. <33 min. 73 ± 15% (16 ± 3 points), P = 0.0005) but not in CONTROL (≥33 min. 74 ± 13% (16 ± 3 points) vs. <33 min. 69 ± 9% (15 ± 2 points), P = 0.11). Within PERI-KOAG, there is a tendency towards higher results within the more experienced group (>5 years), but no significant difference to less (≤5 years) experienced colleagues (87 ± 10% (19 ± 2 points) vs. 78 ± 17% (17 ± 4 points), P = 0.08). However, an association between professional experience and success rate in CONTROL has not been shown (71 ± 8% vs. 70 ± 13%, 16 ± 2 vs. 15 ± 3 points; P = 0.66). CONCLUSIONS: CDSS significantly improved the identification of evidence-based treatment approaches. A precise usage of CDSS is mandatory to maximise efficiency.


Subject(s)
Decision Support Systems, Clinical , Physicians , Anticoagulants/adverse effects , Hospitals, University , Humans , Prospective Studies
7.
Hum Vaccin Immunother ; 18(1): 2040933, 2022 12 31.
Article in English | MEDLINE | ID: covidwho-1852823

ABSTRACT

INTRODUCTION: Human papillomavirus (HPV) vaccination rates are low in young adults. Clinical decision support (CDS) in primary care may increase HPV vaccination. We tested the treatment effect of algorithm-driven, web-based, and electronic health record-linked CDS with or without shared decision-making tools (SDMT) on HPV vaccination rates compared to usual care (UC). METHODS: In a clinic cluster-randomized control trial conducted in a healthcare system serving a largely rural population, we randomized 34 primary care clinic clusters (with three clinics sharing clinicians randomized together) to: CDS; CDS+SDMT; UC. The sample included young adults aged 18-26 due for HPV vaccination with a study index visit from 08/01/2018-03/15/2019 in a study clinic. Generalized linear mixed models tested differences in HPV vaccination status 12 months after index visits by study arm. RESULTS: Among 10,253 patients, 6,876 (65.2%) were due for HPV vaccination, and 5,054 met study eligibility criteria. In adjusted analyses, the HPV vaccination series was completed by 12 months in 2.3% (95% CI: 1.6%-3.2%) of CDS, 1.6% (95% CI: 1.1%-2.3%) of CDS+SDMT, and 2.2% (95% CI: 1.6%-3.0%) of UC patients, and at least one HPV vaccine was received by 12 months in 13.1% (95% CI: 10.6%-16.1%) of CDS, 9.2% (95% CI: 7.3%-11.6%) of CDS+SDMT, and 11.2% (95% CI: 9.1%-13.7%) of UC patients. Differences were not significant between arms. Females, those with prior HPV vaccinations, and those seen at urban clinics had significantly higher odds of HPV vaccination in adjusted models. DISCUSSION: CDS may require optimization for young adults to significantly impact HPV vaccination. TRIAL REGISTRATION: clinicaltrials.gov NCT02986230, 12/6/2016.


Subject(s)
Alphapapillomavirus , Decision Support Systems, Clinical , Papillomavirus Infections , Papillomavirus Vaccines , Delivery of Health Care , Female , Humans , Papillomavirus Infections/prevention & control , Primary Health Care , Vaccination , Young Adult
8.
J Evid Based Dent Pract ; 22(3): 101747, 2022 09.
Article in English | MEDLINE | ID: covidwho-1851472

ABSTRACT

BACKGROUND: Tobacco smoking is the leading cause of disease, death, and disability in the United States. Dental practitioners are advised to provide evidence-based smoking cessation interventions to their patients, yet dental practitioners frequently fail to deliver brief smoking cessation advice. OBJECTIVES: To test whether giving dental practitioners a clinical decisions support (CDS) system embedded in their electronic dental record would increase the rate at which patients who smoke (1) report receiving a brief intervention or referral to treatment during a recent dental visit, (2) taking action related to smoking cessation within 7 days of visit, and (3) stop smoking for 1 day or more or reduce the amount smoked by 50% within 6 months. METHODS: Two-group, parallel arm, cluster-randomized trial. From March through December 2019, 15 nonacademic primary care dental clinics were randomized via covariate adaptive randomization to either a usual care arm or the CDS arm. Adult smokers completed an initial telephone survey within 7 days of their visit and another survey after 6 months. RESULTS: Forty-three patients from 5 CDS and 13 patients from 2 usual care clinics completed the 7-day survey. While the proportion of patients who reported receipt of a brief intervention or referral to treatment was significantly greater in the CDS arm than the usual care arm (84.3% vs 58.6%; P = .005), the differences in percentage of patients who took any action related to smoking cessation within 7 days (44.4% vs 22.3%; P = .077), or stopped smoking for one day or more and/or reduced amount smoked by 50% within 6 months (63.1% vs 46.2%; P = .405) were large but not statistically significant. CONCLUSIONS: Despite interruption by COVID-19, these results demonstrate a promising approach to assist dental practitioners in providing their patients with smoking cessation screening, brief intervention and referral to treatment.


Subject(s)
COVID-19 , Decision Support Systems, Clinical , Smoking Cessation , Adult , Dentists , Humans , Professional Role , Smoking Cessation/methods
9.
Front Public Health ; 10: 861062, 2022.
Article in English | MEDLINE | ID: covidwho-1776092

ABSTRACT

Background and Objective: According to the WHO, diabetes mellitus is a long-term condition marked by high blood sugar levels. The consequences might be far-reaching. According to current increases in mortality, diabetes has risen to number 10 among the leading causes of mortality worldwide. When used to predict diabetes using unbalanced datasets from testing, machine learning (ML) classifiers and established approaches for encoding categorical data have exhibited a broad variety of surprising outcomes. Early studies also made use of an artificial neural network to extract features without obtaining a grasp of the sequence information. Methods: This study offers a deep learning-based decision support system (DSS), utilizing bidirectional long/short-term memory (BiLSTM), to accurately predict diabetic illness from patient data. In order to predict diabetes, the BiLSTM hybrid model was used after balancing the data set. Results: Unlike earlier studies, this proposed model's trial findings were promising, with an accuracy of 93.07%, 93% precision, 92% recall, and a 92% F1-score. Conclusions: Using a BILSTM model for classification outperforms current approaches in the diabetes detection domain.


Subject(s)
Diabetes Mellitus , Algorithms , Decision Support Systems, Clinical , Diabetes Mellitus/diagnosis , Humans , Machine Learning , Neural Networks, Computer
10.
Telemed J E Health ; 28(10): 1470-1478, 2022 10.
Article in English | MEDLINE | ID: covidwho-1766992

ABSTRACT

Introduction: The general practitioners' increasing comprehensive pediatric health care provision in Austria faces great challenges in ensuring high-quality health care in the future as the shortage of pediatricians continues to grow. Tele-expertise services provide an excellent opportunity to facilitate and strengthen interdisciplinary collaboration and access medical expertise of uncertainties in diagnosis and treatment plans. The purpose of this study was to investigate and evaluate the usability, applicability, and clinical advantages of an Austrian tele-expertise platform for doctors, emphasizing its value to strengthen collaborative efforts to extend and ensure quality care in infant, child, and adolescent health while focusing on diagnostic acceleration, verification, and potential modification of a treatment plan. Materials and Methods: A mixed-method approach included the retrospective evaluation of data provided via Intercom to elicit professional and geographical distribution and analysis of four hypotheses (H1: geographic distribution of initial enquires; H2: pediatric expertise level of the requester; H3: teleconsultations will result in changes in diagnosis and therapeutic decisions; and H4: teleconsultations stimulate cooperation and collaboration between physicians of all specialties). The study was based on survey questionnaires and qualitative semi-structured interviews. Discussion: Benefits were the most apparent in shorter diagnosis times, a potential quality increase in care, and cooperative stimulation. Intended therapy plans were found to be more sensitive to modification. Nevertheless, an overall positive attitude toward the teleconsultation chat became obvious. Moreover, the potential regarding quality improvements in pediatric primary care, shorter diagnosis time, and improved treatment options was found. Conclusions: Outcomes are urging Austrian health authorities to establish political and legal structures for appropriate monetary compensation and broad application of an expert consultation system. The article further highlights the importance of teleconsultations in critical situations, such as pandemic times.


Subject(s)
Decision Support Systems, Clinical , General Practitioners , Remote Consultation , Adolescent , Austria , Child , Humans , Primary Health Care , Remote Consultation/methods , Retrospective Studies
11.
Comput Biol Med ; 144: 105381, 2022 05.
Article in English | MEDLINE | ID: covidwho-1773221

ABSTRACT

BACKGROUND: The number of people in the UK with two or more conditions continues to grow and their clinical management is complicated by the reliance on guidance focused on a single condition. This leaves individual clinicians responsible for collating disparate information from patient management systems and care recommendations to manually manage the contradictions that exist in the simultaneous treatment of various conditions. METHODS/DESIGN: We have devised a modelling language based on BPMN that allows us to create computer interpretable representations of single condition guidance and incorporate patient data to detect the points of conflict between multiple conditions based on their transformation to logical constraints. This has been used to develop a prototype clinical decision support tool that we can use to highlight the causes of conflict between them in three main areas: medication, lifestyle and well-being, and appointment bookings. RESULTS: The prototype tool was used to discern contradictions in the care recommendations of chronic obstructive pulmonary disease and osteoarthritis. These were presented to a panel of clinicians who confirmed that the tool produced clinically relevant alerts that can advise clinicians of the presence of conflicts between guidelines relating to both clashes in medication or lifestyle advice. CONCLUSIONS: The need for supporting general practitioners in their treatment of patients remains and this proof of concept has demonstrated that by converting this guidance into computer-interpretable pathways we can use constraint solvers to readily identify clinically relevant points of conflict between critical elements of the pathway.


Subject(s)
Decision Support Systems, Clinical , Pulmonary Disease, Chronic Obstructive , Humans , Morbidity , Negotiating
12.
Am J Health Syst Pharm ; 79(14): 1198-1204, 2022 07 08.
Article in English | MEDLINE | ID: covidwho-1764495

ABSTRACT

PURPOSE: To evaluate the effectiveness of clinical decision support (CDS) alerts tied to high-risk medications at a Michigan health system by determining the true prescriber action rate in response to select "do not give" (DNG) alerts. METHODS: A retrospective review of prescriber actions in response to CDS alerts was conducted to evaluate the effectiveness of alerts designed to prevent prescribing of high-risk medications to patients with concurrent DNG orders. The primary endpoint was the overall action rate, determined by totaling orders cancelled within the alert display and orders modified shortly after an alert. The overall action rate was hypothesized to significantly exceed the action rate estimated on the basis of alert overrides alone. Following the initial review, changes were made to the alert format and preset documentation choices ("acknowledgement comments"), and it was hypothesized that these changes would increase the overall action rate. A repeat analysis was conducted to evaluate the impact of these changes. RESULTS: Across a total of 506 CDS alerts over 14 months, 78% resulted in prescribers modifying orders to comply with alert recommendations. Prescribers cancelled orders in response to only 26% of alerts, often overriding alerts prior to modifying orders. Documentation of rationale or approval for overrides was inconsistent, and while requiring acknowledgement comments facilitated documentation of prescriber rationale, it did not consistently improve overall action rates. CONCLUSION: These findings demonstrate that override rates alone are not good markers for the true effectiveness of CDS alerts and support the need for frequent evaluation of alerts at the institutional level. CDS alerts remain a valuable tool to prevent inappropriate prescribing of high-risk medications and for promoting patient safety.


Subject(s)
Decision Support Systems, Clinical , Electronic Prescribing , Medical Order Entry Systems , Electronic Health Records , Humans , Inappropriate Prescribing , Retrospective Studies
13.
NPJ Prim Care Respir Med ; 32(1): 10, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1740441

ABSTRACT

Dyspnoea or breathlessness is a common presenting symptom among patients attending primary care services. This review aimed to determine whether there are clinical tools that can be incorporated into a clinical decision support system for primary care for efficient and accurate diagnosis of causes of chronic dyspnoea. We searched MEDLINE, EMBASE and Google Scholar for all literature published between 1946 and 2020. Studies that evaluated a clinical algorithm for assessment of chronic dyspnoea in patients of any age group presenting to physicians with chronic dyspnoea were included. We identified 326 abstracts, 55 papers were reviewed, and eight included. A total 2026 patients aged between 20-80 years were included, 60% were women. The duration of dyspnoea was three weeks to 25 years. All studies undertook a stepwise or algorithmic approach to the assessment of dyspnoea. The results indicate that following history taking and physical examination, the first stage should include simply performed tests such as pulse oximetry, spirometry, and electrocardiography. If the patient remains undiagnosed, the second stage includes investigations such as chest x-ray, thyroid function tests, full blood count and NT-proBNP. In the third stage patients are referred for more advanced tests such as echocardiogram and thoracic CT. If dyspnoea remains unexplained, the fourth stage of assessment will require secondary care referral for more advanced diagnostic testing such as exercise tests. Utilising this proposed stepwise approach is expected to ascertain a cause for dyspnoea for 35% of the patients in stage 1, 83% by stage 3 and >90% of patients by stage 4.


Subject(s)
Decision Support Systems, Clinical , Dyspnea , Adult , Aged , Aged, 80 and over , Dyspnea/diagnosis , Dyspnea/etiology , Female , Humans , Middle Aged , Spirometry , Young Adult
14.
J Med Internet Res ; 24(2): e29279, 2022 02 18.
Article in English | MEDLINE | ID: covidwho-1700633

ABSTRACT

BACKGROUND: COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19's effect on patients with certain pre-existing conditions. OBJECTIVE: In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient's electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19. METHODS: The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient's medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient's medical history will affect their COVID-19 outcomes. RESULTS: The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources-enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined. CONCLUSIONS: The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient's medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions.


Subject(s)
COVID-19 , Decision Support Systems, Clinical , Electronic Health Records , Humans , SARS-CoV-2 , Software
15.
PLoS One ; 17(2): e0263898, 2022.
Article in English | MEDLINE | ID: covidwho-1686109

ABSTRACT

Usually, official and survey-based statistics guide policymakers in their choice of response instruments to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected and fast-changing dynamics, data from traditional statistics are only available with non-negligible time delays. This leaves policymakers uncertain about how to most effectively manage their economic countermeasures to support businesses, especially when they need to respond quickly, as in the COVID-19 pandemic. Given this information deficit, we propose a framework that guided policymakers throughout all stages of this unforeseen economic shock by providing timely and reliable sources of firm-level data as a basis to make informed policy decisions. We do so by combining early stage 'ad hoc' web analyses, 'follow-up' business surveys, and 'retrospective' analyses of firm outcomes. A particular focus of our framework is on assessing the early effects of the pandemic, using highly dynamic and large-scale data from corporate websites. Most notably, we show that textual references to the coronavirus pandemic published on a large sample of company websites and state-of-the-art text analysis methods allowed to capture the heterogeneity of the pandemic's effects at a very early stage and entailed a leading indication on later movements in firm credit ratings. While the proposed framework is specific to the COVID-19 pandemic, the integration of results obtained from real-time online sources in the design of subsequent surveys and their value in forecasting firm-level outcomes typically targeted by policy measures, is a first step towards a more timely and holistic approach for policy guidance in times of economic shocks.


Subject(s)
COVID-19/economics , COVID-19/epidemiology , Decision Support Systems, Clinical , Economics , Bankruptcy , Communication , Humans , Internet , Regression Analysis , Risk Assessment , Surveys and Questionnaires
16.
Curr Med Imaging ; 18(2): 104-112, 2022.
Article in English | MEDLINE | ID: covidwho-1624417

ABSTRACT

OBJECTIVE: Coronavirus-related disease, a deadly illness, has raised public health issues worldwide. The majority of individuals infected are multiplying. The government is taking aggressive steps to quarantine people, people exposed to infection, and clinical trials for treatment. Subsequently recommends critical care for the aged, children, and health-care personnel. While machine learning methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." With rapidly growing datasets, there also remain important considerations when developing and validating ML models. METHODS: This paper reviews the recent study that applies machine-learning technology addressing Corona virus-related disease issues' challenges in different perspectives. The report also discusses various treatment trials and procedures on Corona virus-related disease infected patients providing insights to physicians and the public on the current treatment challenges. RESULTS: The paper provides the individual with insights into certain precautions to prevent and control the spread of this deadly disease. CONCLUSION: This review highlights the utility of evidence-based machine learning prediction tools in several clinical settings, and how similar models can be deployed during the Corona virus-related disease pandemic to guide hospital frontlines and health-care administrators to make informed decisions about patient care and managing hospital volume. Further, the clinical trials conducted so far for infected patients with Corona virus-related disease addresses their results to improve community alertness from the viewpoint of a well-known saying, "prevention is always better."


Subject(s)
COVID-19 , Decision Support Systems, Clinical , Aged , Child , Humans , Machine Learning , Pandemics , SARS-CoV-2
17.
PLoS One ; 17(1): e0262193, 2022.
Article in English | MEDLINE | ID: covidwho-1606289

ABSTRACT

OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.


Subject(s)
COVID-19/diagnosis , Decision Support Systems, Clinical , Logistic Models , Machine Learning , Triage/methods , COVID-19/physiopathology , Emergency Service, Hospital , Humans , ROC Curve , Severity of Illness Index
18.
J Med Internet Res ; 23(12): e23571, 2021 12 03.
Article in English | MEDLINE | ID: covidwho-1596242

ABSTRACT

BACKGROUND: There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. OBJECTIVE: The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. METHODS: We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. RESULTS: We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians' reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. CONCLUSIONS: The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.


Subject(s)
Anti-Infective Agents , Decision Support Systems, Clinical , Anti-Bacterial Agents/therapeutic use , Anti-Infective Agents/therapeutic use , Humans , Retrospective Studies
19.
Am J Med Genet A ; 188(4): 1142-1148, 2022 04.
Article in English | MEDLINE | ID: covidwho-1593959

ABSTRACT

We studied if clinicians could gain sufficient working knowledge of a computer-assisted diagnostic decision support system (DDSS) (SimulConsult), to make differential diagnoses (DDx) of genetic disorders. We hypothesized that virtual training could be convenient, asynchronous, and effective in teaching clinicians how to use a DDSS. We determined the efficacy of virtual, asynchronous teaching for clinicians to gain working knowledge to make computer-assisted DDx. Our study consisted of three surveys (Baseline, Training, and After Use) and a series of case problems sent to clinicians at Vanderbilt University Medical Center. All participants were able to generate computer-assisted DDx that achieved passing scores of the case problems. Between 75% and 92% agreed/completely agreed the DDSS was useful to their work and for clinical decision support and was easy to use. Participants' use of the DDSS resulted in statistically significant time savings in key tasks and in total time spent on clinical tasks. Our results indicate that virtual, asynchronous teaching can be an effective format to gain a working knowledge of a DDSS, and its clinical use could result in significant time savings across multiple tasks as well as facilitate synergistic interaction between clinicians and lab specialists. This approach is especially pertinent and offers value amid the COVID-19 pandemic.


Subject(s)
Diagnosis, Computer-Assisted , Genetic Diseases, Inborn/diagnosis , Genetic Diseases, Inborn/genetics , Teaching , User-Computer Interface , Decision Support Systems, Clinical , Diagnosis, Computer-Assisted/methods , Education, Medical , Humans , Physicians , Surveys and Questionnaires
20.
PLoS One ; 16(3): e0247773, 2021.
Article in English | MEDLINE | ID: covidwho-1575465

ABSTRACT

BACKGROUND: The coronavirus infectious disease 19 (COVID-19) pandemic has resulted in significant morbidities, severe acute respiratory failures and subsequently emergency departments' (EDs) overcrowding in a context of insufficient laboratory testing capacities. The development of decision support tools for real-time clinical diagnosis of COVID-19 is of prime importance to assist patients' triage and allocate resources for patients at risk. METHODS AND PRINCIPAL FINDINGS: From March 2 to June 15, 2020, clinical patterns of COVID-19 suspected patients at admission to the EDs of Liège University Hospital, consisting in the recording of eleven symptoms (i.e. dyspnoea, chest pain, rhinorrhoea, sore throat, dry cough, wet cough, diarrhoea, headache, myalgia, fever and anosmia) plus age and gender, were investigated during the first COVID-19 pandemic wave. Indeed, 573 SARS-CoV-2 cases confirmed by qRT-PCR before mid-June 2020, and 1579 suspected cases that were subsequently determined to be qRT-PCR negative for the detection of SARS-CoV-2 were enrolled in this study. Using multivariate binary logistic regression, two most relevant symptoms of COVID-19 were identified in addition of the age of the patient, i.e. fever (odds ratio [OR] = 3.66; 95% CI: 2.97-4.50), dry cough (OR = 1.71; 95% CI: 1.39-2.12), and patients older than 56.5 y (OR = 2.07; 95% CI: 1.67-2.58). Two additional symptoms (chest pain and sore throat) appeared significantly less associated to the confirmed COVID-19 cases with the same OR = 0.73 (95% CI: 0.56-0.94). An overall pondered (by OR) score (OPS) was calculated using all significant predictors. A receiver operating characteristic (ROC) curve was generated and the area under the ROC curve was 0.71 (95% CI: 0.68-0.73) rendering the use of the OPS to discriminate COVID-19 confirmed and unconfirmed patients. The main predictors were confirmed using both sensitivity analysis and classification tree analysis. Interestingly, a significant negative correlation was observed between the OPS and the cycle threshold (Ct values) of the qRT-PCR. CONCLUSION AND MAIN SIGNIFICANCE: The proposed approach allows for the use of an interactive and adaptive clinical decision support tool. Using the clinical algorithm developed, a web-based user-interface was created to help nurses and clinicians from EDs with the triage of patients during the second COVID-19 wave.


Subject(s)
COVID-19 Testing , COVID-19/diagnosis , Decision Support Systems, Clinical , Adult , Aged , Cough/diagnosis , Dyspnea/diagnosis , Female , Fever/diagnosis , Headache/diagnosis , Hospitals , Humans , Male , Middle Aged , Pharyngitis/diagnosis , SARS-CoV-2/isolation & purification
SELECTION OF CITATIONS
SEARCH DETAIL