Novel tech throws knock-out punch to ECG improving GP referral decisions to cardiology.

PURPOSE: In a comparator study, designed with assistance from the Food and Drug Administration, a State-of-the-Art (SOTA) ECG device augmented with automated analysis, the comparator, was compared with a breakthrough technology, Cardio-HART (CHART). METHODS: The referral decision defined by physician reading biosignal-based ECG or CHART report were compared for 550 patients, where its performance is calculated against the ground truth referral decision. The ground truth was established by cardiologist consensus based on all the available measurements and findings including echocardiography (ECHO). RESULTS: The results confirmed that CHART analysis was far more effective than ECG only analysis: CHART reduced false negative rates 15.8% and false positive (FP) rates by 5%, when compared with SOTA ECG devices. General physicians (GP's) using CHART saw their positive diagnosis rate significantly increased, from ~10% to ~26% (260% increase), and the uncertainty rate significantly decreased, from ~31% to ~1.9% (94% decrease). For cardiology, the study showed that in 98% of the cases, the CHART report was found to be a good indicator as to what kind of heart problems can be expected (the 'start-point') in the ECHO examination. CONCLUSIONS: The study revealed that GP use of CHART resulted in more accurate referrals for cardiology, resulting in fewer true negative or FP-healthy or mildly abnormal patients not in need of ECHO confirmation. The indirect benefit is the reduction in wait-times and in unnecessary and costly testing in secondary care. Moreover, when used as a start-point, CHART can shorten the echocardiograph examination time.

Pharmacogenomic testing to support prescribing in primary care: a structured review of implementation models.

The application of pharmacogenomics could meaningfully contribute toward medicines optimization within primary care. This review identified 13 studies describing eight implementation models utilizing a multi-gene pharmacogenomic panel within a primary care or community setting. These were small feasibility studies (n <200). They demonstrated importance and feasibility of pre-test counseling, the role of the pharmacist, data integration into the electronic medical record and point-of-care clinical decision support systems (CDSS). Findings were considered alongside existing primary care prescribing practices and implementation frameworks to demonstrate how issues may be addressed by existing nationalized healthcare and primary care infrastructure. Development of point-of-care CDSS should be prioritized; establishing clinical leadership, education programs, defining practitioner roles and responsibilities and addressing commissioning issues will also be crucial.

Using machine learning to improve risk prediction in durable left ventricular assist devices.

Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683-0.730] versus 0.740 [0.717-0.762] and 1-year: 0.691 [0.673-0.710] versus 0.714 [0.695-0.734]; each p<0.001). Net reclassification index analysis similarly demonstrated an improvement of 48.8% and 36.9% for 90-day and 1-year mortality, respectively, with extreme gradient boosting (each p<0.001). Concordance in predicted risk between logistic regression and extreme gradient boosting resulted in substantially improved c-index for both logistic regression and extreme gradient boosting (90-day logistic regression 0.536 versus 0.752, 1-year logistic regression 0.555 versus 0.726, 90-day extreme gradient boosting 0.623 versus 0.772, 1-year extreme gradient boosting 0.613 versus 0.742, each p<0.001). These results demonstrate that machine learning can improve risk model performance for durable left ventricular assist devices, both independently and as an adjunct to logistic regression.

Effects of patients' hospital discharge preferences on uptake of clinical decision support.

The Centers for Medicare and Medicaid Services identified unplanned hospital readmissions as a critical healthcare quality and cost problem. Improvements in hospital discharge decision-making and post-discharge care are needed to address the problem. Utilization of clinical decision support (CDS) can improve discharge decision-making but little is known about the empirical significance of two opposing problems that can occur: (1) negligible uptake of CDS by providers or (2) over-reliance on CDS and underuse of other information. This paper reports an experiment where, in addition to electronic medical records (EMR), clinical decision-makers are provided subjective reports by standardized patients, or CDS information, or both. Subjective information, reports of being eager or reluctant for discharge, was obtained during examinations of standardized patients, who are regularly employed in medical education, and in our experiment had been given scripts for the experimental treatments. The CDS tool presents discharge recommendations obtained from econometric analysis of data from de-identified EMR of hospital patients. 38 clinical decision-makers in the experiment, who were third and fourth year medical students, discharged eight simulated patient encounters with an average length of stay 8.1 in the CDS supported group and 8.8 days in the control group. When the recommendation was "Discharge," CDS uptake of "Discharge" recommendation was 20% higher for eager than reluctant patients. Compared to discharge decisions in the absence of patient reports: (i) odds of discharging reluctant standardized patients were 67% lower in the CDS-assisted group and 40% lower in the control (no-CDS) group; whereas (ii) odds of discharging eager standardized patients were 75% higher in the control group and similar in CDS-assisted group. These findings indicate that participants were neither ignoring nor over-relying on CDS.

Can Big Data guide prognosis and clinical decisions in epilepsy?

Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

Artificial intelligence for decision support in acute stroke - current roles and potential.

The identification and treatment of patients with stroke is becoming increasingly complex as more treatment options become available and new relationships between disease features and treatment response are continually discovered. Consequently, clinicians must constantly learn new skills (such as clinical evaluations or image interpretation), stay up to date with the literature and incorporate advances into everyday practice. The use of artificial intelligence (AI) to support clinical decision making could reduce inter-rater variation in routine clinical practice and facilitate the extraction of vital information that could improve identification of patients with stroke, prediction of treatment responses and patient outcomes. Such support systems would be ideal for centres that deal with few patients with stroke or for regional hubs, and could assist informed discussions with the patients and their families. Moreover, the use of AI for image processing and interpretation in stroke could provide any clinician with an imaging assessment equivalent to that of an expert. However, any AI-based decision support system should allow for expert clinician interaction to enable identification of errors (for example, in automated image processing). In this Review, we discuss the increasing importance of imaging in stroke management before exploring the potential and pitfalls of AI-assisted treatment decision support in acute stroke.

What should primary care look like after the COVID-19 pandemic?

The response to COVID-19 transformed primary care: new telehealth items were added to the Medicare Benefits Schedule, and their use quickly escalated, general practices and community health centres developed new ways of working and patients embraced the changes. As new coronavirus infections plummet and governments contemplate lifting spatial distancing restrictions, attention should turn to the transition out of pandemic mode. Some good things happened during the pandemic, including the rapid introduction of the new telehealth items. The post-pandemic health system should learn from the COVID-19 changes and create a new normal.

The impact of a computerised decision support system on antibiotic usage in an English hospital.

Background Antimicrobial resistance is correlated with the inappropriate use of antibiotics. Computerised decision support systems may help practitioners to make evidence-based decisions when prescribing antibiotics. Objective This study aimed to evaluate the impact of computerized decision support systems on the volume of antibiotics used. Setting A very large 1200-bed teaching hospital in Birmingham, England. Main outcome measure The primary outcome measure was the defined daily doses/1000 occupied bed-days. Method A retrospective longitudinal study was conducted to examine the impact of computerised decision support systems on the volume of antibiotic use. The study compared two periods: one with computerised decision support systems, which lasted for 2 years versus one without which lasted for 2 years after the withdrawal of computerised decision support systems. Antibiotic use data from June 2012 to June 2016 were analysed (comprising 2 years with computerised decision support systems immediately followed by 2 years where computerised decision support systems had been withdrawn). Regression analysis was applied to assess the change in antibiotic consumption through the period of the study. Result From June 2012 to June 2016, total antibiotic usage increased by 13.1% from 1436 to 1625 defined daily doses/1000 bed-days: this trend of increased antibiotic prescribing was more pronounced following the withdrawal of structured prescribing (computerised decision support systems). There was a difference of means of - 110.14 defined daily doses/1000 bed days of the total usage of antibiotics in the period with and without structured prescribing, and this was statistically significant (p = 0.026). From June 2012 to June 2016, the dominant antibiotic class used was penicillins. The trends for the total consumption of all antibiotics demonstrated an increase of use for all antibiotic classes except for tetracyclines, quinolones, and anti-mycobacterial drugs, whereas aminoglycoside usage remained stable. Conclusion The implementation of computerised decision support systems appears to influence the use of antibiotics by reducing their consumption. Further research is required to determine the specific features of computerised decision support systems, which influence increased higher adoption and uptake of this technology.

Clinical Decision Support: The Law, the Future, and the Role for Radiologists.

Clinical Decision Support (CDS) was designed as an interactive, electronic tool for use by clinicians that communicates Appropriate Use Criteria (AUC) information to the user and assists them in making the most appropriate treatment decision for a patient's specific clinical condition. Policymakers recognized AUC as a potential solution to control inappropriate utilization of imaging and made CDS mandatory in the Protecting Access to Medicare Act of 2014. In the years since Protecting Access to Medicare Act, data on the potential impact of CDS has been mixed and much of the physician community has expressed concern about the logistics of the program. This article aims to review the legislation behind the AUC program, the events that have transpired since, and some of the challenges and opportunities facing radiologists in the current environment.

Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience.

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

Forecasting Content and Stage in a Nursing Home Information Technology Maturity Instrument Using a Delphi Method.

Health information technology capabilities in some healthcare sectors, such as nursing homes, are not well understood because measures for information technology uptake have not been fully developed, tested, validated, or measured consistently. The paper provides a report of the development and testing of a new instrument measuring nursing home information technology maturity and stage of maturity. Methods incorporated a four round Delphi panel composed of 31 nursing home experts from across the nation who reported the highest levels of information technology sophistication in a separate national survey. Experts recommended 183 content items for 27 different content areas specifying the measure of information technology maturity. Additionally, experts ranked each of the 183 content items using an IT maturity instrument containing seven stages (stages 0-6) of information technology maturity. The majority of content items (40% (n = 74)) were associated with information technology maturity stage 4, corresponding to facilities with external connectivity capability. Over 11% of the content items were at the highest maturity stage (Stage 5 and 6). Content areas with content items at the highest stage of maturity are reflected in nursing homes that have technology available for residents or their representatives and used extensively in resident care. An instrument to assess nursing home IT maturity and stage of maturity has important implications for understanding health service delivery systems, regulatory efforts, patient safety and quality of care.

Measures of self-efficacy among doctors in conducting an online search for clinical decision making.

OBJECTIVES: To measure the perceived ability and level of confidence among doctors in performing the different tasks involved in conducting an online search for clinical decision making. METHODS: A large-scale cross-sectional survey was conducted in 36 District Headquarter Hospitals (DHQs), 89 Tehsil Headquarter Hospitals (THQs), 293 Rural Health Centers (RHCs) and 2455 Basic Health Units (BHUs) in Punjab, Pakistan. Using a quota sampling, data were collected from 517 doctors on a set of 11 statements. The collected data were analysed statistically. RESULTS: Of the 517 doctors, 73 (14.1%) had 'never accessed health care information online' for clinical decision making. Mean values of the doctors' response to the 11 statements ranged from 1.66 to 2.30 indicating that most of the doctors were 'not confident' in their ability to perform the tasks. CONCLUSION: The majority of doctors perceived themselves able to perform the different tasks involved in conducting an online search. Age and working experience were significant factors in the perception of their ability in performing the tasks. The study recommends promotional and educational activities to motivate interest, increase awareness, develop knowledge and skills for doctors to access information that would help in their clinical decision making.

Evolution of Diabetes Technology.

Technological innovations have fundamentally changed diabetes care. Insulin pump use and continuous glucose monitoring are associated with improved glycemic control along with a better quality of life; automated insulin-dosing advisors facilitate and improve decision making. Glucose-responsive automated insulin delivery enables the highest targets for time in range, lowest rate and duration of hypoglycemia, and favorable quality of life. Clear targets for time in ranges and a standard visualization of the data will help the diabetes technology to be used more efficiently. Decision support systems within and integrated cloud environment will further simplify, unify, and improve modern routine diabetes care.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.

BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). OBJECTIVES: We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. SOURCES: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. CONTENT: We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). IMPLICATIONS: Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

The Ottawa subarachnoid hemorrhage clinical decision rule for classifying emergency department headache patients.

BACKGROUND: Subarachnoid hemorrhage (SAH) is a serious cause of headaches. The Ottawa subarachnoid hemorrhage (OSAH) rule helps identify SAH in patients with acute nontraumatic headache with high sensitivity, but provides limited information for identifying other intracranial pathology (ICP). OBJECTIVES: To assess the performance of the OSAH rule in emergency department (ED) headache patients and evaluate its impact on the diagnosis of intracranial hemorrhage (ICH) and other ICP. METHOD: We conducted a retrospective cohort study from January 2016 to March 2017. Patients with acute headache with onset within 14 days of the ED visit, were included. We excluded patients with head trauma that occurred in the previous 7 days, new onset of abnormal neurologic findings, or consciousness disturbance. According to the OSAH rule, patients with any included predictors required further investigation. RESULTS: Of 913 patients were included, 15 of them were diagnosed with SAH. The OSAH rule had 100% (95% CI, 78.2%-100%) sensitivity and 37.0% (95% CI, 33.8-40.2%) specificity for identifying SAH. Twenty-two cases were identified as SAH or ICH with 100% sensitivity (95% CI, 84.6%-100%) and 37.3% (95% CI, 34.1%-40.5%) specificity. As for non-hemorrhagic ICP, both the sensitivity and negative predictive values (NPV) decreased to 75.0% (95% CI, 53.3%-90.2%) and 98.2% (95% CI, 96.1%-99.3%), respectively. CONCLUSIONS: The OSAH rule had 100% sensitivity and NPV for diagnosing SAH and ICH with acute headache. The sensitivity and specificity were lower for non-hemorrhagic ICP. The OSAH rule may be an effective tool to exclude acute ICH and SAH in our setting.