Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

INTRODUCTION: Every year 2.4 million deaths occur worldwide in babies younger than 28 days. Approximately 70% of these deaths occur in low-resource settings because of failure to implement evidence-based interventions. Digital health technologies may offer an implementation solution. Since 2014, we have worked in Bangladesh, Malawi, Zimbabwe and the UK to develop and pilot Neotree: an android app with accompanying data visualisation, linkage and export. Its low-cost hardware and state-of-the-art software are used to improve bedside postnatal care and to provide insights into population health trends, to impact wider policy and practice. METHODS AND ANALYSIS: This is a mixed methods (1) intervention codevelopment and optimisation and (2) pilot implementation evaluation (including economic evaluation) study. Neotree will be implemented in two hospitals in Zimbabwe, and one in Malawi. Over the 2-year study period clinical and demographic newborn data will be collected via Neotree, in addition to behavioural science informed qualitative and quantitative implementation evaluation and measures of cost, newborn care quality and usability. Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies. ETHICS AND DISSEMINATION: This is a Wellcome Trust funded project (215742_Z_19_Z). Research ethics approvals have been obtained: Malawi College of Medicine Research and Ethics Committee (P.01/20/2909; P.02/19/2613); UCL (17123/001, 6681/001, 5019/004); Medical Research Council Zimbabwe (MRCZ/A/2570), BRTI and JREC institutional review boards (AP155/2020; JREC/327/19), Sally Mugabe Hospital Ethics Committee (071119/64; 250418/48). Results will be disseminated via academic publications and public and policy engagement activities. In this study, the care for an estimated 15 000 babies across three sites will be impacted. TRIAL REGISTRATION NUMBER: NCT0512707; Pre-results.

Unexpected Inequality: Disparate-Impact From Artificial Intelligence in Healthcare Decisions.

Systemic discrimination in healthcare plagues marginalized groups. Physicians incorrectly view people of color as having high pain tolerance, leading to undertreatment. Women with disabilities are often undiagnosed because their symptoms are dismissed. Low-income patients have less access to appropriate treatment. These patterns, and others, reflect long-standing disparities that have become engrained in U.S. health systems. As the healthcare industry adopts artificial intelligence and algorithminformed (AI) tools, it is vital that regulators address healthcare discrimination. AI tools are increasingly used to make both clinical and administrative decisions by hospitals, physicians, and insurers--yet there is no framework that specifically places nondiscrimination obligations on AI users. The Food and Drug Administration has limited authority to regulate AI and has not sought to incorporate anti-discrimination principles in its guidance. Section 1557 of the Affordable Care Act has not been used to enforce nondiscrimination in healthcare AI and is under-utilized by the Office of Civil Rights. State level protections by medical licensing boards or malpractice liability are similarly untested and have not yet extended nondiscrimination obligations to AI. This Article discusses the role of each legal obligation on healthcare AI and the ways in which each system can improve to address discrimination. It highlights the ways in which industries can self-regulate to set nondiscrimination standards and concludes by recommending standards and creating a super-regulator to address disparate impact by AI. As the world moves towards automation, it is imperative that ongoing concerns about systemic discrimination are removed to prevent further marginalization in healthcare.

Accuracy assessment methods for physiological model selection toward evaluation of closed-loop controlled medical devices.

Physiological closed-loop controlled (PCLC) medical devices are complex systems integrating one or more medical devices with a patient's physiology through closed-loop control algorithms; introducing many failure modes and parameters that impact performance. These control algorithms should be tested through safety and efficacy trials to compare their performance to the standard of care and determine whether there is sufficient evidence of safety for their use in real care setting. With this aim, credible mathematical models have been constructed and used throughout the development and evaluation phases of a PCLC medical device to support the engineering design and improve safety aspects. Uncertainties about the fidelity of these models and ambiguities about the choice of measures for modeling performance need to be addressed before a reliable PCLC evaluation can be achieved. This research develops tools for evaluating the accuracy of physiological models and establishes fundamental measures for predictive capability assessment across different physiological models. As a case study, we built a refined physiological model of blood volume (BV) response by expanding an original model we developed in our prior work. Using experimental data collected from 16 sheep undergoing hemorrhage and fluid resuscitation, first, we compared the calibration performance of the two candidate physiological models, i.e., original and refined, using root-mean-squared error (RMSE), Akiake information criterion (AIC), and a new multi-dimensional approach utilizing normalized features extracted from the fitting error. Compared to the original model, the refined model demonstrated a significant improvement in calibration performance in terms of RMSE (9%, P = 0.03) and multi-dimensional measure (48%, P = 0.02), while a comparable AIC between the two models verified that the enhanced calibration performance in the refined model is not due to data over-fitting. Second, we compared the physiological predictive capability of the two models under three different scenarios: prediction of subject-specific steady-state BV response, subject-specific transient BV response to hemorrhage perturbation, and leave-one-out inter-subject BV response. Results indicated enhanced accuracy and predictive capability for the refined physiological model with significantly larger proportion of measurements that were within the prediction envelope in the transient and leave-one-out prediction scenarios (P < 0.02). All together, this study helps to identify and merge new methods for credibility assessment and physiological model selection, leading to a more efficient process for PCLC medical device evaluation.

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper.

The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist. In this AMIA position paper, the authors describe current and emerging challenges to the safe use of Adaptive CDS and lay out recommendations for the effective management and monitoring of Adaptive CDS.

Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review.

BACKGROUND: The benefits of data and analytics for health care systems and single providers is an increasingly investigated field in digital health literature. Electronic health records (EHR), for example, can improve quality of care. Emerging analytics tools based on artificial intelligence show the potential to assist physicians in day-to-day workflows. Yet, single health care providers also need information regarding the economic impact when deciding on potential adoption of these tools. OBJECTIVE: This paper examines the question of whether data and analytics provide economic advantages or disadvantages for health care providers. The goal is to provide a comprehensive overview including a variety of technologies beyond computer-based patient records. Ultimately, findings are also intended to determine whether economic barriers for adoption by providers could exist. METHODS: A systematic literature search of the PubMed and Google Scholar online databases was conducted, following the hermeneutic methodology that encourages iterative search and interpretation cycles. After applying inclusion and exclusion criteria to 165 initially identified studies, 50 were included for qualitative synthesis and topic-based clustering. RESULTS: The review identified 5 major technology categories, namely EHRs (n=30), computerized clinical decision support (n=8), advanced analytics (n=5), business analytics (n=5), and telemedicine (n=2). Overall, 62% (31/50) of the reviewed studies indicated a positive economic impact for providers either via direct cost or revenue effects or via indirect efficiency or productivity improvements. When differentiating between categories, however, an ambiguous picture emerged for EHR, whereas analytics technologies like computerized clinical decision support and advanced analytics predominantly showed economic benefits. CONCLUSIONS: The research question of whether data and analytics create economic benefits for health care providers cannot be answered uniformly. The results indicate ambiguous effects for EHRs, here representing data, and mainly positive effects for the significantly less studied analytics field. The mixed results regarding EHRs can create an economic barrier for adoption by providers. This barrier can translate into a bottleneck to positive economic effects of analytics technologies relying on EHR data. Ultimately, more research on economic effects of technologies other than EHRs is needed to generate a more reliable evidence base.

Triage of patients with venous and lymphatic diseases during the COVID-19 pandemic - The Venous and Lymphatic Triage and Acuity Scale (VELTAS) : A consensus document of the International Union of Phlebology (UIP), Australasian College of Phlebology (ACP), American Vein and Lymphatic Society (AVLS), American Venous Forum (AVF), European College of Phlebology (ECoP), European Venous Forum (EVF), Interventional Radiology Society of Australasia (IRSA), Latin American Venous Forum, Pan-American Society of Phlebology and Lymphology and the Venous Association of India (VAI) This consensus document has been co-published in Phlebology [DOI: 10.1177/0268355520930884] and Journal of Vascular Surgery: Venous and Lymphatic Disorders [DOI: 10.1016/j.jvsv.2020.05.002]. The publications are identical except for minor stylistic and spelling differences in keeping with each journal's style. The contribution has been published under a Attribution-Non Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0), (https://creativecommons.org/licenses/by-nc-nd/4.0/).

The coronavirus disease 2019 (COVID-19) global pandemic has resulted in diversion of healthcare resources to the management of patients infected with SARS-CoV-2 virus. Elective interventions and surgical procedures in most countries have been postponed and operating room resources have been diverted to manage the pandemic. The Venous and Lymphatic Triage and Acuity Scale was developed to provide an international standard to rationalise and harmonise the management of patients with venous and lymphatic disorders or vascular anomalies. Triage urgency was determined based on clinical assessment of urgency with which a patient would require medical treatment or surgical intervention. Clinical conditions were classified into six categories of: (1) venous thromboembolism (VTE), (2) chronic venous disease, (3) vascular anomalies, (4) venous trauma, (5) venous compression and (6) lymphatic disease. Triage urgency was categorised into four groups and individual conditions were allocated to each class of triage. These included (1) medical emergencies (requiring immediate attendance), example massive pulmonary embolism; (2) urgent (to be seen as soon as possible), example deep vein thrombosis; (3) semi-urgent (to be attended to within 30-90 days), example highly symptomatic chronic venous disease, and (4) discretionary/non-urgent- (to be seen within 6-12 months), example chronic lymphoedema. Venous and Lymphatic Triage and Acuity Scale aims to standardise the triage of patients with venous and lymphatic disease or vascular anomalies by providing an international consensus-based classification of clinical categories and triage urgency. The scale may be used during pandemics such as the current COVID-19 crisis but may also be used as a general framework to classify urgency of the listed conditions.

An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making.

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to 'hybrid' BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted.

Economic value of clinical decision support allied to direct data feedback to clinicians: blood usage in haematology.

BACKGROUND AND OBJECTIVES: Responding to national and local pressures to reduce the amount of blood transfused, the haematology department of Oxford University Hospitals (OUH), UK implemented an electronic blood-ordering system with clinical decision support. This intervention targeted junior doctors, giving regular feedback on their transfusion practices with respect to clinical guidelines. METHODS: We evaluated the incremental costs of the intervention using interrupted time series methods to compare red blood cell and platelet usage before and after the intervention was implemented. Difference-in-differences analysis was used to control for external factors that would affect the use of blood products over time. Reductions in blood usage were balanced against intervention costs. RESULTS: The base case analysis showed an average cost saving to the department of £89 304 annually as a result of the intervention. Scenario analyses suggested that the savings may have been greater still, had the increasing trend in blood use prior to the intervention continued in the absence of the intervention. CONCLUSION: An electronic blood-ordering system with clinical decision support can reduce blood transfusions and associated healthcare costs. Focusing on improving junior doctors' transfusion practice is expected to have a knock-on benefit in terms of dissemination of good transfusion practice both within their own department and others as they continue their training.

Adoption of coronary artery disease - Reporting and Data System (CAD-RADS™) and observed impact on medical therapy and systolic blood pressure control.

BACKGROUND: CAD-RADS was developed to standardize communication of per-patient maximal stenosis on coronary CT angiography (CCTA) and provide treatment recommendations and may impact primary prevention care and resource utilization. The authors sought to evaluate CAD-RADS adoption on preventive medical therapy and risk factor control amongst a mixed provider population. METHODS: Statins, aspirin (ASA), systolic blood pressure and, when available, lipid panel changes were abstracted for 1796 total patients undergoing CCTA in the 12 months before (non-standard reporting, NSR, cohort) and after adoption of the CAD-RADS reporting template. Only initiation of a medication in a treatment naïve patient, escalation from baseline dose, or transition to a higher potency was considered an escalation/initiation in lipid therapy. RESULTS: The CAD-RADS reporting template was utilized in 83.7% (751/897) of CCTAs after the CAD-RADS adoption period. After adjusting for any coronary artery disease (CAD) on CCTA, statin initiation/escalation was more commonly observed in the CAD-RADS cohort (aOR 1.46; 95%CI 1.12-1.90, p = 0.005), driven by higher rates of new statin initiation (aOR 1.79; 95%CI 1.23-2.58, p = 0.002). This resulted in a higher observed rates of total cholesterol improvement in the CAD-RADS cohort (58% vs 49%, p = 0.016). New ASA initiation was similar between reporting templates after adjustment for CAD on CCTA (aOR 1.40; 95%CI 0.97-2.02, p = 0.069). The ordering provider's specialty (cardiology vs non-cardiology) did not significantly impact the observed differences in initiation/escalation of statins and ASA (pinteraction = NS). CONCLUSIONS: Adoption of CAD-RADS reporting was associated with increased utilization of preventive medications, regardless of ordering provider specialty.

Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting.

BACKGROUND: Periodontitis (PD), a form of gum disease, is a major public health concern as it is globally prevalent and harms both individual quality of life and economic productivity. Global cost in lost productivity is estimated at US$54 billion annually. Moreover, current PD assessment applies only after the damage has already occurred. OBJECTIVE: This study proposes and tests a new PD risk assessment model applicable at point-of-care, using supervised machine learning methods. METHODS: We compare the performance of five algorithms using retrospective clinical data: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT). RESULTS: DT and ANN demonstrated higher accuracy in classifying the patients with high or low PD risk as compared to NB, LR and SVM. The resultant model with DT showed a sensitivity of 87.08% (95% CI 84.12% to 89.76%) and specificity of 93.5% (95% CI 91% to 95.49%). CONCLUSIONS: A predictive model with high sensitivity and specificity to stratify individuals into low and high PD risk tiers was developed. Validation in other populations will inform translational value of this approach and its potential applicability as clinical decision support tool.

Reducing waste: a guidelines-based approach to reducing inappropriate vitamin D and TSH testing in the inpatient rehabilitation setting.

Background: Laboratory overutilisation increases healthcare costs, and can lead to overdiagnosis, overtreatment and negative health outcomes. Discipline-specific guidelines do not support routine testing for Vitamin D and thyroid-stimulating hormone (TSH) in the inpatient rehabilitation setting, yet 94% of patients had Vitamin D and TSH tests on admission to inpatient rehabilitation at our institution. Our objective was to reduce Vitamin D and TSH testing by 25% on admission to inpatient Stroke, Spinal Cord Injury, Acquired Brain Injury and Amputee Rehabilitation units. Methods: A fishbone framework for root cause analysis revealed potential causes underlying overutilisation of Vitamin D and TSH testing. A series of Plan-Do-Study-Act (PDSA) cycles were introduced to target remediable factors, starting with an academic detailing intervention with key stakeholders that reviewed applicable clinical guidelines for each patient care discipline and the rationale for reducing admission testing. Simultaneously, computerised clinical decision support (CCDS) limited Vitamin D testing to specific criteria. Audit and feedback were used in a subsequent PDSA cycle. Frequency of Vitamin D and TSH testing on admission was the primary outcome measure. The number of electronic admission order caresets containing automatic Vitamin D and/or TSH orders before and after the interventions was the process measure. Rate of Vitamin D supplementation and changes in thyroid-related medication were the balancing measures. Results: After implementation, 2.9% of patients had admission Vitamin D testing (97% relative reduction) and 53% of patients had admission TSH testing (43% relative reduction). Admission order caresets with prepopulated Vitamin D and TSH orders decreased from 100% (n=6) to 0%. The interventions were successful; similar to previous literature, CCDS was more effective than education and audit and feedback interventions alone. The interventions represent >$9000 annualised savings.

Strategies for Stakeholder Engagement and Uptake of New Intervention: Experience From State-Wide Implementation of mHealth Technology for NCD Care in Tripura, India.

BACKGROUND: Appropriate strategies and key stakeholder engagement are the keys to successful implementation of new health care interventions. OBJECTIVES: The study sought to articulate the key strategies used for scaling up a research-based intervention, mPower Heart electronic Clinical Decision Support System (e-CDSS), for state-wide implementation at health facilities in Tripura. METHODS: Multiple strategies were used for statewide implementation of mPower Heart e-CDSS at noncommunicable diseases clinics across the government health facilities in Tripura: formation of a technical coordination-cum-support unit, change management, enabling environment, adapting the intervention with user focus, and strengthening the Health Information System. RESULTS: The effective delivery of a new health system intervention requires engagement at multiple levels including political leadership, health administrators, and health professionals, which can be achieved by forming a technical coordination-cum-support unit. It is important to specify the role and responsibilities of existing manpower and provide a structured training program. Enabling environment at health facilities (providing essential equipment, space and time, etc.) is also crucial. Successful implementation also requires that patients, health care providers, the health system, and leadership recognize the immediate and long-term benefits of the new intervention and have a buy-in in the intervention. With constant encouragement and nudge from administrative authorities and by using multiple strategies, 40 government health facilities adopted the mPower Heart e-CDSS. From its launch in May 2017 until November 20, 2018, a total of 100,810 eligible individuals were screened and enrolled, with 35,884 treated for hypertension, 9,698 for diabetes, and 5,527 for both hypertension and diabetes. CONCLUSIONS: Multiple strategies, based on implementation principles, are required for successful scaling up of research-based interventions.

Clinical relevance of alerts from a decision support system, PHARAO, for drug safety assessment in the older adults.

BACKGROUND: PHARAO is a decision support system developed to evaluate the risk for a set of either common or serious side-effects resulting from a combination of pharmacodynamic effects from a patient's medications. The objective of this study was to investigate the validity of the risk scores for the common side-effects generated by PHARAO in older patients. METHODS: Side-effects included were sedation, constipation, orthostatic symptoms, anticholinergic and serotonergic effects. The alerts generated by PHARAO were tested in 745 persons ≥65 years old. Dispensed prescriptions retrieved from the Swedish prescribed drug register were used to generate the pharmacological risk scores of patients' medications. Symptoms possibly related to side-effects were extracted from medical records data. RESULTS: The PHARAO system generated 776 alerts, most often for the risk of anticholinergic symptoms. The total specificity estimates of the PHARAO system were 0.95, 0.89 and 0.78 for high, intermediate and low risk alerts, respectively. The corresponding sensitivity estimates were between 0.12 and 0.37. The negative predictive value was 0.90 and the positive predictive value ranged between 0.20-0.25. CONCLUSIONS: The PHARAO system had a high specificity and negative predictive value to detect symptoms possibly associated with the of patients' medications, while the sensitivity and positive predictive value were low. The PHARAO system has the potential to minimise the risk of over-alerts in combination with a drug-drug interaction alert system, but should be used in connection with a medical evaluation of the patient.

Using Mobile Health to Support Clinical Decision-Making to Improve Maternal and Neonatal Health Outcomes in Ghana: Insights of Frontline Health Worker Information Needs.

BACKGROUND: Developing and maintaining resilient health systems in low-resource settings like Ghana requires innovative approaches that adapt technology to context to improve health outcomes. One such innovation was a mobile health (mHealth) clinical decision-making support system (mCDMSS) that utilized text messaging (short message service, SMS) of standard emergency maternal and neonatal protocols via an unstructured supplementary service data (USSD) on request of the health care providers. This mCDMSS was implemented in a cluster randomized controlled trial (CRCT) in the Eastern Region of Ghana. OBJECTIVE: This study aimed to analyze the pattern of requests made to the USSD by health workers (HWs). We assessed the relationship between requests made to the USSD and types of maternal and neonatal morbidities reported in health facilities (HFs). METHODS: For clusters in the intervention arm of the CRCT, all requests to the USSD during the 18-month intervention period were extracted from a remote server, and maternal and neonatal health outcomes of interest were obtained from the District Health Information System of Ghana. Chi-square and Fisher exact tests were used to compare the proportion and type of requests made to the USSD by cluster, facility type, and location; whether phones accessing the intervention were shared facility phones or individual-use phones (type-of-phone); or whether protocols were accessed during the day or at night (time-of-day). Trends in requests made were analyzed over 3 6-month periods. The relationship between requests made and the number of cases reported in HFs was assessed using Spearman correlation. RESULTS: In total, 5329 requests from 72 (97%) participating HFs were made to the intervention. The average number of requests made per cluster was 667. Requests declined from the first to the third 6-month period (44.96% [2396/5329], 39.82% [2122/5329], and 15.22% [811/5329], respectively). Maternal conditions accounted for the majority of requests made (66.35% [3536/5329]). The most frequently accessed maternal conditions were postpartum hemorrhage (25.23% [892/3536]), other conditions (17.82% [630/3536]), and hypertension (16.49% [583/3536]), whereas the most frequently accessed neonatal conditions were prematurity (20.08% [360/1793]), sepsis (15.45% [277/1793]), and resuscitation (13.78% [247/1793]). Requests made to the mCDMSS varied significantly by cluster, type of request (maternal or neonatal), facility type and its location, type-of-phone, and time-of-day at 6-month interval (P<.001 for each variable). Trends in maternal and neonatal requests showed varying significance over each 6-month interval. Only asphyxia and sepsis cases showed significant correlations with the number of requests made (r=0.44 and r=0.79; P<.001 and P=.03, respectively). CONCLUSIONS: There were variations in the pattern of requests made to the mCDMSS over time. Detailed information regarding the use of the mCDMSS provides insight into the information needs of HWs for decision-making and an opportunity to focus support for HW training and ultimately improved maternal and neonatal health.

Concordance Rate between Clinicians and Watson for Oncology among Patients with Advanced Gastric Cancer: Early, Real-World Experience in Korea.

Backgrounds/Aims: Watson for Oncology (WFO) is a cognitive technology that processes medical information by analyzing the latest evidence and guidelines. However, studies of the concordance rate between WFO and clinicians for advanced gastric cancer (AGC) are lacking. Methods: We retrospectively reviewed 65 patients with AGC who consulted WFO and the Gachon Gil Medical Center multidisciplinary team (GMDT) in 2016 and 2017. The recommendations of WFO were compared with the opinions of the GMDT. WFO provided three treatment options: recommended (first treatment option), for consideration (second treatment option), and not recommended. Results: In total, 65 patients (mean age 61.0 years; 44 males and 21 females) were included in the study. The concordance rate between WFO and the GMDT was 41.5% (27/65) at the recommended level and 87.7% (57/65) at the for consideration level. The main causes of discordance between WFO and the GMDT were as follows. First, WFO did not consider the medical history. Second, WFO recommended the use of agents that are considered outdated in Korea. Third, some patients wanted to be involved in a clinical trial. Fourth, some patients refused to use the biologic agents recommended by WFO for financial reasons as they were not covered by medical insurance. Conclusions: The concordance rate at the recommended level was relatively low but was higher at the for consideration level. Discordances arose mainly from the different medical circumstances at the Gachon Gil Medical Center (GMC) and the Memorial Sloan Kettering Cancer Center (MSKCC), the main WFO consulting center. The utility of WFO as a tool for supporting clinical decision making could be further improved by incorporating regional guidelines.

Effects of a standardized emergency department protocol on after-hours use of venous duplex ultrasound.

OBJECTIVE: Vascular laboratory (VL) venous duplex ultrasound is the "gold standard" for diagnosis of lower extremity deep venous thrombosis (DVT), which is linked to many morbid conditions. Decreasing night and weekend use of VL services in the emergency department (ED) represents a potentially viable means of reducing costs as skilled personnel must remain on call and receive a wage premium when activated. We investigated the effects of workflow changes that required ED providers to use a computerized decision-making tool, integrated into the electronic medical record, to calculate a Wells score for each patient considered for an after-hours venous duplex ultrasound study for suspected DVT. METHODS: The rate of VL use and study positivity before and after implementation of the decision-making tool were examined in addition to measures of ED throughput, rate of concomitant pulmonary embolism, disposition of examined patients from the ED, observed thrombus distribution in duplex ultrasound studies positive for DVT, and calculated personnel costs of after-hours VL use. RESULTS: A total of 391 after-hours, ED-initiated venous duplex ultrasound studies were obtained during the 4-year study period (n = 213 before intervention, n = 178 after intervention; P = .12). Whereas the period immediately after the start of the intervention saw a decrease in VL use, this was not sustained. Studies performed after the intervention were not more likely to be positive for acute DVT (12.2% vs 18%; P = .1179). The average Wells score was 2.8 (range, 0-6). VL personnel were called in 347 times during the 4-year period, with a total cost of $14,643.40. Nurse-ordered studies were significantly more likely to be positive, with 22% revealing acute DVT compared with 12% for physician-ordered studies (P = .042). The intervention resulted in significant improvements in ED throughput, with time between triage and study request falling from 226 minutes to 165 minutes (P < .001). Observed thrombus distribution revealed involvement of the most proximal external iliac system in a minority of cases (11%), whereas most thrombi (89%) were limited to the femoropopliteal, calf, and superficial venous systems. CONCLUSIONS: A requirement for ED providers to document a Wells score before obtaining an after-hours venous duplex ultrasound study resulted in only a transient decrease in VL use but improved ED throughput. Studies ordered by nurses were significantly more likely to be positive, possibly as a result of consistent protocol adherence compared with the physicians. Future studies may warrant investigation into this provider variance.

Assessment of Different Methods for Pediatric Meningitis Dosing Clinical Decision Support.

BACKGROUND: Indication-specific medication dosing support is needed to improve pediatric dosing support. OBJECTIVE: To compare the sensitivity and positive predictive value (PPV) of different meningitis dosing alert triggers and dosing error rates between antimicrobials with and without meningitis order sentences. METHODS: We retrospectively analyzed 4-months of pediatric orders for antimicrobials with meningitis-specific dosing. At the time of the order, it was determined if the antimicrobial was for meningitis management, if a cerebrospinal fluid (CSF) culture was ordered, and if a natural language processing (NLP) system could detect "meningitis" in clinical notes. RESULTS: Of 1383 orders, 243 were for the management of meningitis. A CSF culture or NLP combination trigger searching the electronic health record since admission yielded the greatest sensitivity for detecting meningitis management (67.5%, P < 0.01 vs others), but dosing error detection was similar if the trigger only searched 48 hours preceding the order (68.8% vs 62.5%, P = 0.125). Using a CSF culture alone and a 48-hour time frame had a higher PPV versus a combination with a 48-hour time frame (97.1% vs 80.9%, P < 0.001), and both triggers had a higher PPV than others ( P < 0.001). Antimicrobials with meningitis order sentences had fewer dosing errors (19.8% vs 43.2%, P < 0.01). Conclusion and Relevance: A meningitis dosing alert triggered by a combination of a CSF culture or NLP system and a 48-hour triggering time frame could provide reasonable sensitivity and PPV for meningitis dosing errors. Order sentences with indication-specific recommendations may provide additional dosing support, but additional studies are needed.

Legal and ethical issues surrounding the use of crowdsourcing among healthcare providers.

As the pace of medical discovery widens the knowledge-to-practice gap, technologies that enable peer-to-peer crowdsourcing have become increasingly common. Crowdsourcing has the potential to help medical providers collaborate to solve patient-specific problems in real time. We recently conducted the first trial of a mobile, medical crowdsourcing application among healthcare providers in a university hospital setting. In addition to acknowledging the benefits, our participants also raised concerns regarding the potential negative consequences of this emerging technology. In this commentary, we consider the legal and ethical implications of the major findings identified in our previous trial including compliance with the Health Insurance Portability and Accountability Act, patient protections, healthcare provider liability, data collection, data retention, distracted doctoring, and multi-directional anonymous posting. We believe the commentary and recommendations raised here will provide a frame of reference for individual providers, provider groups, and institutions to explore the salient legal and ethical issues before they implement these systems into their workflow.

The NASSS framework for ex post theorisation of technology-supported change in healthcare: worked example of the TORPEDO programme.

BACKGROUND: Evaluation of health technology programmes should be theoretically informed, interdisciplinary, and generate in-depth explanations. The NASSS (non-adoption, abandonment, scale-up, spread, sustainability) framework was developed to study unfolding technology programmes in real time-and in particular to identify and manage their emergent uncertainties and interdependencies. In this paper, we offer a worked example of how NASSS can also inform ex post (i.e. retrospective) evaluation. METHODS: We studied the TORPEDO (Treatment of Cardiovascular Risk in Primary Care using Electronic Decision Support) research programme, a multi-faceted computerised quality improvement intervention for cardiovascular disease prevention in Australian general practice. The technology (HealthTracker) had shown promise in a cluster randomised controlled trial (RCT), but its uptake and sustainability in a real-world implementation phase was patchy. To explain this variation, we used NASSS to undertake secondary analysis of the multi-modal TORPEDO dataset (results and process evaluation of the RCT, survey responses, in-depth professional interviews, videotaped consultations) as well as a sample of new, in-depth narrative interviews with TORPEDO researchers. RESULTS: Ex post analysis revealed multiple areas of complexity whose influence and interdependencies helped explain the wide variation in uptake and sustained use of the HealthTracker technology: the nature of cardiovascular risk in different populations, the material properties and functionality of the technology, how value (financial and non-financial) was distributed across stakeholders in the system, clinicians' experiences and concerns, organisational preconditions and challenges, extra-organisational influences (e.g. policy incentives), and how interactions between all these influences unfolded over time. CONCLUSION: The NASSS framework can be applied retrospectively to generate a rich, contextualised narrative of technology-supported change efforts and the numerous interacting influences that help explain its successes, failures, and unexpected events. A NASSS-informed ex post analysis can supplement earlier, contemporaneous evaluations to uncover factors that were not apparent or predictable at the time but dynamic and emergent.