System-Agnostic Clinical Decision Support Services: Benefits and Challenges for Scalable Decision Support

System-agnostic clinical decision support (CDS) services provide patient evaluation capabilities that are independent of specific CDS systems and system implementation contexts. While such system-agnostic CDS services hold great potential for facilitating the widespread implementation of CDS systems, little has been described regarding the benefits and challenges of their use. In this manuscript, the authors address this need by describing potential benefits and challenges of using a system-agnostic CDS service. This analysis is based on the authors’ formal assessments of, and practical experiences with, various approaches to developing, implementing, and maintaining CDS capabilities. In particular, the analysis draws on the authors’ experience developing and leveraging a system-agnostic CDS Web service known as SEBASTIAN. A primary potential benefit of using a system-agnostic CDS service is the relative ease and flexibility with which the service can be leveraged to implement CDS capabilities across applications and care settings. Other important potential benefits include facilitation of centralized knowledge management and knowledge sharing; the potential to support multiple underlying knowledge representations and knowledge resources through a common service interface; improved simplicity and componentization; easier testing and validation; and the enabling of distributed CDS system development. Conversely, important potential challenges include the increased effort required to develop knowledge resources capable of being used in many contexts and the critical need to standardize the service interface. Despite these challenges, our experiences to date indicate that the benefits of using a system-agnostic CDS service generally outweigh the challenges of using this approach to implementing and maintaining CDS systems

The effect of feedback to general practitioners on quality of care for people with type 2 diabetes. A systematic review of the literature

<p>Abstract</p> <p>Background</p> <p>There have been numerous efforts to improve and assure the quality of treatment and follow-up of people with Type 2 diabetes (PT2D) in general practice. Facilitated by the increasing usability and validity of guidelines, indicators and databases, feedback on diabetes care is a promising tool in this aspect. Our goal was to assess the effect of feedback to general practitioners (GPs) on the quality of care for PT2D based on the available literature.</p> <p>Methods</p> <p>Systematic review searches were conducted using October 2008 updates of Medline (Pubmed), Cochrane library and Embase databases. Additional searches in reference lists and related articles were conducted. Papers were included if published in English, performed as randomized controlled trials, studying diabetes, having general practice as setting and using feedback to GPs on diabetes care. The papers were assessed according to predefined criteria.</p> <p>Results</p> <p>Ten studies complied with the inclusion criteria. Feedback improved the care for PT2D, particularly process outcomes such as foot exams, eye exams and Hba1c measurements. Clinical outcomes like lowering of blood pressure, Hba1c and cholesterol levels were seen in few studies. Many process and outcome measures did not improve, while none deteriorated. Meta analysis was unfeasible due to heterogeneity of the studies included. Two studies used electronic feedback.</p> <p>Conclusion</p> <p>Based on this review, feedback seems a promising tool for quality improvement in diabetes care, but more research is needed, especially of electronic feedback.</p

Patient access to complex chronic disease records on the internet

Background: Access to medical records on the Internet has been reported to be acceptable and popular with patients, although most published evaluations have been of primary care or office-based practice. We tested the feasibility and acceptability of making unscreened results and data from a complex chronic disease pathway (renal medicine) available to patients over the Internet in a project involving more than half of renal units in the UK. Methods: Content and presentation of the Renal PatientView (RPV) system was developed with patient groups. It was designed to receive information from multiple local information systems and to require minimal extra work in units. After piloting in 4 centres in 2005 it was made available more widely. Opinions were sought from both patients who enrolled and from those who did not in a paper survey, and from staff in an electronic survey. Anonymous data on enrolments and usage were extracted from the webserver. Results: By mid 2011 over 17,000 patients from 47 of the 75 renal units in the UK had registered. Users had a wide age range (&#60;10 to &#62;90 yrs) but were younger and had more years of education than non-users. They were enthusiastic about the concept, found it easy to use, and 80% felt it gave them a better understanding of their disease. The most common reason for not enrolling was being unaware of the system. A minority of patients had security concerns, and these were reduced after enrolling. Staff responses were also strongly positive. They reported that it aided patient concordance and disease management, and increased the quality of consultations with a neutral effect on consultation length. Neither patient nor staff responses suggested that RPV led to an overall increase in patient anxiety or to an increased burden on renal units beyond the time required to enrol each patient. Conclusions: Patient Internet access to secondary care records concerning a complex chronic disease is feasible and popular, providing an increased sense of empowerment and understanding, with no serious identified negative consequences. Security concerns were present but rarely prevented participation. These are powerful reasons to make this type of access more widely available

Protocol for the 'e-Nudge trial' : a randomised controlled trial of electronic feedback to reduce the cardiovascular risk of individuals in general practice [ISRCTN64828380]

Background: Cardiovascular disease (including coronary heart disease and stroke) is a major cause of death and disability in the United Kingdom, and is to a large extent preventable, by lifestyle modification and drug therapy. The recent standardisation of electronic codes for cardiovascular risk variables through the United Kingdom's new General Practice contract provides an opportunity for the application of risk algorithms to identify high risk individuals. This randomised controlled trial will test the benefits of an automated system of alert messages and practice searches to identify those at highest risk of cardiovascular disease in primary care databases. Design: Patients over 50 years old in practice databases will be randomised to the intervention group that will receive the alert messages and searches, and a control group who will continue to receive usual care. In addition to those at high estimated risk, potentially high risk patients will be identified who have insufficient data to allow a risk estimate to be made. Further groups identified will be those with possible undiagnosed diabetes, based either on elevated past recorded blood glucose measurements, or an absence of recent blood glucose measurement in those with established cardiovascular disease. Outcome measures: The intervention will be applied for two years, and outcome data will be collected for a further year. The primary outcome measure will be the annual rate of cardiovascular events in the intervention and control arms of the study. Secondary measures include the proportion of patients at high estimated cardiovascular risk, the proportion of patients with missing data for a risk estimate, and the proportion with undefined diabetes status at the end of the trial

Patient attitudes toward using computers to improve health services delivery

BACKGROUND: The aim of this study was to examine the acceptability of point of care computerized prompts to improve health services delivery among a sample of primary care patients. METHODS: Primary data collection. Cross-sectional survey. Patients were surveyed after their visit with a primary care provider. Data were obtained from patients of ten community-based primary care practices in the spring of 2001. RESULTS: Almost all patients reported that they would support using a computer before each visit to prompt their doctor to: "do health screening tests" (92%), "counsel about health behaviors (like diet and exercise)" (92%) and "change treatments for health conditions" (86%). In multivariate testing, the only variable that was associated with acceptability of the point of care computerized prompts was patient's confidence in their ability to answer questions about their health using a computer (beta = 0.39, p = .001). Concerns about data security were expressed by 36.3% of subjects, but were not related to acceptability of the prompts. CONCLUSIONS: Support for using computers to generate point of care prompts to improve quality-oriented processes of care was high in our sample, but may be contingent on patients feeling familiar with their personal medical history

The quality case for information technology in healthcare

BACKGROUND: As described in the Institute of Medicine's Crossing the Quality Chasm report, the quality of health care in the U.S. today leaves much to be desired. DISCUSSION: One major opportunity for improving quality relates to increasing the use of information technology, or IT. Health care organizations currently invest less in IT than in any other information-intensive industry, and not surprisingly current systems are relatively primitive, compared with industries such as banking or aviation. Nonetheless, a number of organizations have demonstrated that quality can be substantially improved in a variety of ways if IT use is increased in ways that improve care. Specifically, computerization of processes that are error-prone and computerized decision support may substantially improve both efficiency and quality, as well as dramatically facilitate quality measurement. This report discusses the current levels of IT and quality in health care, how quality improvement and management are currently done, the evidence that more IT might be helpful, a vision of the future, and the barriers to getting there. SUMMARY: This report suggests that there are five key policy domains that need to be addressed: standards, incentives, security and confidentiality, professional involvement, and research, with financial incentives representing the single most important lever

Evaluation of an online interactive Diabetes Needs Assessment Tool (DNAT) versus online self-directed learning: a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Methods for the dissemination, understanding and implementation of clinical guidelines need to be examined for their effectiveness to help doctors integrate guidelines into practice. The objective of this randomised controlled trial was to evaluate the effectiveness of an interactive online Diabetes Needs Assessment Tool (DNAT) (which constructs an e-learning curriculum based on individually identified knowledge gaps), compared with self-directed e-learning of diabetes guidelines.</p> <p>Methods</p> <p>Health professionals were randomised to a 4-month learning period and either given access to diabetes learning modules alone (control group) or DNAT plus learning modules (intervention group). Participants completed knowledge tests before and after learning (primary outcome), and surveys to assess the acceptability of the learning and changes to clinical practice (secondary outcomes).</p> <p>Results</p> <p>Sixty four percent (677/1054) of participants completed both knowledge tests. The proportion of nurses (5.4%) was too small for meaningful analysis so they were excluded. For the 650 doctors completing both tests, mean (SD) knowledge scores increased from 47.4% (12.6) to 66.8% (11.5) [intervention group (n = 321, 64%)] and 47.3% (12.9) to 67.8% (10.8) [control group (n = 329, 66%)], (ANCOVA p = 0.186). Both groups were satisfied with the usability and usefulness of the learning materials. Seventy seven percent (218/284) of the intervention group reported combining the DNAT with the recommended reading materials was "<it>very useful"/"useful"</it>. The majority in both groups (184/287, 64.1% intervention group and 206/299, 68.9% control group) [95% CI for the difference (-2.8 to 12.4)] reported integrating the learning into their clinical practice.</p> <p>Conclusions</p> <p>Both groups experienced a similar and significant improvement in knowledge. The learning materials were acceptable and participants incorporated the acquired knowledge into practice.</p> <p>Trial registration</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN67215088">ISRCTN67215088</a></p

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

Standards for Scalable Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal for Progress

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

<p>Abstract</p> <p>Background</p> <p>In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.</p> <p>Discussion</p> <p>Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the <it>Roadmap for National Action on Clinical Decision Support </it>commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.</p> <p>Summary</p> <p>A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.</p