Feasibility and impact of an evidence-based electronic decision support system for diabetes care in family medicine: protocol for a cluster randomized controlled trial

Background: In Belgium, the construction of the national electronic point-of-care information service, EBMPracticeNet, was initiated in 2011 to optimize quality of care by promoting evidence-based decision-making. The collaboration of the government, healthcare providers, Evidence-Based Medicine (EBM) partners, and vendors of Electronic Health Records (EHR) is unique to this project. All Belgian healthcare professionals get free access to an up-to-date database of validated Belgian and nearly 1,000 international guidelines, incorporated in a portal that also provides EBM information from sources other than guidelines, including computerized clinical decision support that is integrated in the EHRs. The EBMeDS system is the electronic evidence-based decision support system of EBMPracticeNet. The EBMeDS system covers all clinical areas of diseases and could play a crucial role in response to the emerging challenge posed by chronic conditions. Diabetes was chosen as the analysis topic of interest. The objective of this study is to assess the effectiveness of EBMeDS use in improving diabetes care. This objective will be enhanced by a formal process evaluation to provide crucial information on the feasibility of using the system in daily Belgian family medicine. Methods: The study is a cluster-randomized trial with before/after measurements conducted in Belgian family medicine. Physicians' practices will be randomly assigned to the intervention or control group in a 1: 1 ratio, to receive either the EBMeDS reminders or to follow the usual care process. Randomization will be performed by a statistical consultant with an electronic random list generator, anonymously for the researchers. The follow-up period of the study will be 12 months with interim analysis points at 3, 6 and 9 months. Primary outcome is the one-year pre- to post-implementation change in HbA1c. Patients will not be informed about the intervention. Data analysts will be kept blinded to the allocation. Discussion: The knowledge obtained in this study will be useful for further integration in other Belgian software packages. Users' perceptions and process evaluation will provide information for improving the feasibility of the system

A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study

[Background] The evidence-based medicine (EBM) paradigm requires the development of health care professionals’ skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients’ health care, increase patient safety, and optimize resources use.[Objective] The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice.[Methods] Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy.[Results] The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times.[Conclusions] The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future.This project has received funding from the Andalusian Ministry of Health from Spain (reference PIN-0213-2016), and FEDER funds.Peer reviewe

Templates as a method for implementing data provenance in decision support systems

AbstractDecision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates – abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project

Developing an electronic health record (EHR) for methadone treatment recording and decision support

Background: in this paper, we give an overview of methadone treatment in Ireland and outline the rationale for designing an electronic health record (EHR) with extensibility, interoperability and decision support functionality. Incorporating several international standards, a conceptual model applying a problem orientated approach in a hierarchical structure has been proposed for building the EHR.Methods: a set of archetypes has been designed in line with the current best practice and clinical guidelines which guide the information-gathering process. A web-based data entry system has been implemented, incorporating elements of the paper-based prescription form, while at the same time facilitating the decision support function.Results: the use of archetypes was found to capture the ever changing requirements in the healthcare domain and externalises them in constrained data structures. The solution is extensible enabling the EHR to cover medicine management in general as per the programme of the HRB Centre for Primary Care Research.Conclusions: the data collected via this Irish system can be aggregated into a larger dataset, if necessary, for analysis and evidence-gathering, since we adopted the openEHR standard. It will be later extended to include the functionalities of prescribing drugs other than methadone along with the research agenda at the HRB Centre for Primary Care Research in Irelan

Self-reported use and clinical usefulness of second-generation decision support – a survey at the pilot sites for Evidence-Based Medicine elec-tronic Decision Support (EBMeDS)

In Finland, electronic decision support is evolving from searchable knowledge bases toward integration of the knowledge modules into the electronic health record in the Evidence-Based Medicine electronic Decision Support project. We conducted a baseline survey on the extent of use of second-generation decision support (electronic databases) by the various categories of health care professionals. The results showed that the majority of health care professionals used the electronic databases in their clinical practice; more than 80% of participating physicians, registered nurses, public health nurses, and ward nurses used at least four databases. In addition, the respondents considered these databases clinical useful in their practice. This indicates that health care professionals seem to be ready for the third-generation clinical decision support system, producing, for example, automatic reminders

Improving Delivery of Evidence-Based Prenatal Care in a Family Medicine Clinic

Background: According to the Institute of Medicine, using evidence-based decision-making is one of the key principles that will enable the health care system to provide consistent, high-quality medical care to all people. This can be a challenge when providing care to pregnant women, as ethical issues regarding research in this population have resulted in a relative dearth of high quality randomized control trials providing evidence for prenatal issues. The challenge of providing evidence-based prenatal care is further compounded in a busy Family Medicine teaching practice where pregnant women represent a relatively small fraction of the patients seen on a daily basis. Purpose: The purpose of this project was to develop concise, evidence-based protocols for the management of several common prenatal problems and implement them in a Family Medicine clinic in order to improve the quality of care provided to pregnant women in this practice. Methods: First, four common prenatal problems were identified: 1) Obesity in Pregnancy; 2) Prior Preterm Labor; 3) Gestational Diabetes; and 4) Chronic Hypertension in Pregnancy. For each of these problems, a comprehensive review of the literature was performed. Priority was given to guidelines from professional organizations, meta-analyses and randomized control trials. Using the strongest evidence from all of these sources, a one-page protocol was developed for each condition. The protocols then underwent a review process by the physicians at the clinic. In areas where no strong evidence existed, medico-legal considerations and consensus-derived provider preferences were incorporated into the protocols. Results: In each of the four problems of interest, there existed at least one recommendation that had strong evidence to support it. These recommendations included screening tools, counseling topics and pharmacologic interventions. Conclusions: The first phase of this project has resulted in the development of concise, evidence-based protocols for care of patients with four common prenatal problems that can now be instituted in the Family Medicine clinic. These protocols incorporate the strongest evidence available, and on issues where no strong evidence is available, they take into consideration medico-legal issues and provider preferences derived from a consensus process. We hope that the availability of these protocols will result in more consistent, evidence-based prenatal care. The next steps will be to assess provider utilization of and satisfaction with the protocols, as well as gather outcomes data to see if the implementation of these protocols results in better patient outcomes

Clinical decision support in emergency medicine : exploring the prerequisites

A clinical decision support system is a technical system that combines individual patient data and evidence-based clinical knowledge to give advice and support to clinicians. For quite a long time, the emergence of such systems has been predicted and expected to impact health care dramatically by improving both quality and productivity. Three factors make Swedish emergency medicine an interesting context which could be mature for the introduction of clinical decision support systems. Firstly, Sweden is a leader in the implementation of health care information technology, and the coverage of electronic health records is around 100% in the country. Secondly, emergency medicine is a field with high patient turnover, frequent decisions, and substantial impact on patient outcome. Thirdly, although there are abundant publications on clinical decision support system development and implementation in general, there is less knowledge of such systems in the urgent care context. Therefore, this doctoral project aimed to explore the prerequisites prior to implementation of clinical decision support systems in emergency medicine. This thesis is based on a mixed-methods design and consists of four individual studies. Proctor’s conceptual model of implementation research was used as a framework for the project. Study I included semi-structured interviews with 16 medical doctors and nurses from nine Swedish emergency departments. Content analysis was used to describe factors affecting vital sign data quality in emergency care. Study II extracted vital signs from 330 000 emergency department visits to assess the effects of different documentation workflows on data quality. Study III prospectively explored 200 vital sign measurements from 50 emergency care visits to evaluate the impact of manual and automated documentation on vital sign data quality. Study III also used data from an adapted NASA TLX questionnaire to compare the workload of clinical staff (n=70) in manual and automatic documentation. Study IV used semi-structured interviews with 14 emergency medicine physicians from three different sites. Content analysis was used to explore participants’ expectations and concerns regarding clinical decision support systems. There are three main results and conclusions from the research. Firstly, documentation of vital signs in the emergency department is still surprisingly paper-based, which makes vital sign data unfit for reuse in clinical decision support. Secondly, automation of vital sign documentation is feasible in emergency care and should improve data quality and reduce workload. Thirdly, enthusiasts towards decision support are at risk of disappointment with the level of innovation in the currently available decision support systems, and this may affect the implementation strategy negatively

A dashboard-based system for supporting diabetes care

[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159S538547255Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534. doi:10.1136/jamia.2001.0080527Palmer, A. 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International Journal of Medical Informatics, 90, 58-67. doi:10.1016/j.ijmedinf.2016.03.007Sáenz, A., Brito, M., Morón, I., Torralba, A., García-Sanz, E., & Redondo, J. (2012). Development and Validation of a Computer Application to Aid the Physician’s Decision-Making Process at the Start of and during Treatment with Insulin in Type 2 Diabetes: A Randomized and Controlled Trial. Journal of Diabetes Science and Technology, 6(3), 581-588. doi:10.1177/193229681200600313Ampudia-Blasco, F. J., Benhamou, P. Y., Charpentier, G., Consoli, A., Diamant, M., Gallwitz, B., … Stoevelaar, H. (2015). A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes. Diabetes Technology & Therapeutics, 17(3), 194-202. doi:10.1089/dia.2014.0260Lim, S., Kang, S. M., Shin, H., Lee, H. J., Won Yoon, J., Yu, S. H., … Jang, H. C. (2011). Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System. Diabetes Care, 34(2), 308-313. doi:10.2337/dc10-1447Lipton, J. A., Barendse, R. J., Akkerhuis, K. M., Schinkel, A. F. L., & Simoons, M. L. (2010). Evaluation of a Clinical Decision Support System for Glucose Control. Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine, 9(3), 140-147. doi:10.1097/hpc.0b013e3181e7d7caNeubauer, K. M., Mader, J. K., Höll, B., Aberer, F., Donsa, K., Augustin, T., … Pieber, T. R. (2015). Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards. Diabetes Technology & Therapeutics, 17(10), 685-692. doi:10.1089/dia.2015.0027Rodbard, D., & Vigersky, R. A. (2011). Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 5(2), 402-411. doi:10.1177/193229681100500230Augstein, P., Vogt, L., Kohnert, K.-D., Heinke, P., & Salzsieder, E. (2010). Translation of Personalized Decision Support into Routine Diabetes Care. Journal of Diabetes Science and Technology, 4(6), 1532-1539. doi:10.1177/193229681000400631Reza, A. W., & Eswaran, C. (2009). A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy. Journal of Medical Systems, 35(1), 17-24. doi:10.1007/s10916-009-9337-yKumar, S. J. J., & Madheswaran, M. (2012). An Improved Medical Decision Support System to Identify the Diabetic Retinopathy Using Fundus Images. Journal of Medical Systems, 36(6), 3573-3581. doi:10.1007/s10916-012-9833-3Cho, B. H., Yu, H., Kim, K.-W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008). Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. 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M., Moore, J. L., Mehr, D. R., Wakefield, D. S., Yadamsuren, B., … Belden, J. L. (2011). A Diabetes Dashboard and Physician Efficiency and Accuracy in Accessing Data Needed for High-Quality Diabetes Care. The Annals of Family Medicine, 9(5), 398-405. doi:10.1370/afm.1286Den Ouden, H., Vos, R. C., Reidsma, C., & Rutten, G. E. (2015). Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences: design of a cluster randomised (OPTIMAL) trial. BMC Family Practice, 16(1). doi:10.1186/s12875-015-0230-0Holbrook, A., Thabane, L., Keshavjee, K., Dolovich, L., Bernstein, B., … Chan, D. (2009). Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. Canadian Medical Association Journal, 181(1-2), 37-44. doi:10.1503/cmaj.081272O’Reilly, D., Holbrook, A., Blackhouse, G., Troyan, S., & Goeree, R. (2012). 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Implementation of an Emergency Department Computer System: Design Features That Users Value. The Journal of Emergency Medicine, 41(6), 693-700. doi:10.1016/j.jemermed.2010.05.014Sprague, A. E., Dunn, S. I., Fell, D. B., Harrold, J., Walker, M. C., Kelly, S., & Smith, G. N. (2013). Measuring Quality in Maternal-Newborn Care: Developing a Clinical Dashboard. Journal of Obstetrics and Gynaecology Canada, 35(1), 29-38. doi:10.1016/s1701-2163(15)31045-8WILBANKS, B. A., & LANGFORD, P. A. (2014). A Review of Dashboards for Data Analytics in Nursing. CIN: Computers, Informatics, Nursing, 32(11), 545-549. doi:10.1097/cin.0000000000000106Hartzler, A. L., Izard, J. P., Dalkin, B. L., Mikles, S. P., & Gore, J. L. (2015). Design and feasibility of integrating personalized PRO dashboards into prostate cancer care. Journal of the American Medical Informatics Association, 23(1), 38-47. doi:10.1093/jamia/ocv101Dixon, B. E., Jabour, A. M., Phillips, E. O., & Marrero, D. G. (2014). An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data. Journal of the American Medical Informatics Association, 21(3), 517-521. doi:10.1136/amiajnl-2013-001959Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267-298. doi:10.1016/0933-3657(95)00036-4Sacchi, L., Capozzi, D., Bellazzi, R., & Larizza, C. (2015). JTSA: An open source framework for time series abstractions. Computer Methods and Programs in Biomedicine, 121(3), 175-188. doi:10.1016/j.cmpb.2015.05.006Dagliati, A., Sacchi, L., Zambelli, A., Tibollo, V., Pavesi, L., Holmes, J. H., & Bellazzi, R. (2017). Temporal electronic phenotyping by mining careflows of breast cancer patients. Journal of Biomedical Informatics, 66, 136-147. doi:10.1016/j.jbi.2016.12.012Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Bijlsma, M. J., Janssen, F., & Hak, E. (2015). Estimating time-varying drug adherence using electronic records: extending the proportion of days covered (PDC) method. Pharmacoepidemiology and Drug Safety, 25(3), 325-332. doi:10.1002/pds.3935Robusto, F., Lepore, V., D’Ettorre, A., Lucisano, G., De Berardis, G., Bisceglia, L., … Nicolucci, A. (2016). The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level. 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Opening Access To Practice-based Evidence in Clinical Decision Support Systems with Natural Query Language

Evidence-based medicine can be effective only if constantly tested against errors in medical practice. Clinical record database summarization supported by a machine allows allow to detect anomalies and therefore help detect the errors in early phases of care. Summarization system is a part of Clinical Decision Support Systems however it cannot be used directly by the stakeholder as long as s/he is not able to query the clinical record database. Natural Query Languages allow opening access to data for clinical practitioners, that usually do not have knowledge about articial query languages. Results: We have developed general purpose reporting system called Ask Data Anything (ADA) that we applied to a particular CDSS implementation. As a result, we obtained summarization system that opens the access for these of clinical researchers that were excluded from the meaningful summary of clinical records stored in a given clinical database. The most significant part of the component - NQL parser - is a hybrid of Controlled Natural Language (CNL) and pattern matching with a prior error repair phase. Equipped with reasoning capabilities due to the intensive use of semantic technologies, our hybrid approach allows one to use very simple, keyword-based (even erroneous) queries as well as complex CNL ones with the support of a predictive editor. By using ADA sophisticated summarizations of clinical data are produced as a result of NQL query execution. In this paper, we will present the main ideas underlying ADA component in the context of CDSS

Introducing First Year Medical Students to Personalized Medicine Concepts in a Small Group Activity

Presented as a Poster Presentation at 2020 IUSM Education Day.An individuals’ genetic profile is becomingly an increasingly important parameter in healthcare decisions. This small group activity was developed to introduce first year medical students in the Molecules to Cells and Tissues course to the concept and significance of Pharmacogenomics and personalized medicine. Additionally, this activity provided students with an opportunity to work with a large dataset and use the information to impact clinical decision making. This activity has two cases, takes student groups approximately 2 hours to complete, and requires internet access. Case materials are available through the learning management system Canvas, and include open-ended questions to guide students through the cases. In these cases students explore the functional significance of different alleles of a panel of cytochrome P450 genes. The group activity has the students examine a large data set of cytochrome P450 genes and cognate alleles to determine their prevalence in the local population and calculate the individuals’ gene scores. The students are then asked to explain the impact of the genotype (or gene score) on the resulting patient phenotype (i.e. the functional significance of the genotype). The first case involves a breast cancer survivor support group in which patients taking Taxol discuss lack of adequate pain relief from opioids and the potential impact of concomitant use of natural compounds/supplements on drug metabolism. The second case involves a patient presenting with recurrent stroke-like symptoms despite being on the anticoagulant medication clopidogrel. The patient is initially suspected to be non-compliant, but is later determined to be a poor metabolizer of the anticoagulant clopidogrelto its active form thus decreasing its efficacy. The expertise of the IUSM Medical Genetics research faculty was leveraged to provide a large data set of cytochrome P450 genes and cognate alleles. The selection of cytochrome P450 was based upon delivering content focused on the biochemistry of the enzyme system and provided an opportunity to highlight the drug interaction database available through IUSM Clinical Pharmacology (The FlockhartTable™ ; https://drug-interactions.medicine.iu.edu/). The addition of natural compounds was to draw students’ attention to the Natural Medicines database, which is the recommended source for evidence-based data on complementary and alternative medicine. Natural Medicines is available through the Ruth Lilly Medical Library and can be searched by substance or condition. It provides both a summary of the literature available on substances as well as the level of evidence or quality of studies done on the substance