An efficacy driven approach for medication recommendation in type 2 diabetes treatment using data mining techniques

We demonstrate how data mining techniques can help recommend effective medications when physicians need to control the glucose level of patients with type 2 diabetes. We first identify the factors that may affect physicians' medication decisions and then develop a patient-similarity based approach to automatically recommend medications for a patient with the specific condition so that his blood glucose level (measured by HbA1C value) can be well controlled. The approach is validated through experiments on real data sets and compared with the recommendations by following a clinical guideline.

Towards capturing innovation effects of a CDSS (NjuRen)

The e-service NjuRen is a clinical decision support system used by physicians to calculate patients' renal function and provide support for selection of appropriate drug and dosage for patients with renal failure. Project NjuRen is a collaboration between Stockholm County Council and Jönköping International Business School and aims at evaluating the socio-economic impact of implementing IT-systems in healthcare. The project consist of several steps, first the development and adaptation of a model to measure innovation effects. In the second step the development of a survey to capture factual impacts and effects. Finally, in the third step to translate the effects into socio-economic terms. The result will help decision makers to identify the achieved benefits and outcomes that the implementation of the system has brought with it.

Statistical text classifier to detect specific type of medical incidents

WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.

PPADS: Physician-PArent Decision-Support for Neonatal Intensive Care

Family-centered care is becoming the new standard for Neonatal Intensive Care Unit (NICU) patients. In support of this, we developed the Physician PArent Decision Support System (PPADS), which provides clinical updates and predictions of clinical outcomes for infants in the NICU to the neonatologists, and provides an aid to parents for making difficult decisions on the direction of care of their infant with the health care team. The tool may lead to earlier intervention, better allocation of resources, and reduction of the negative outcomes. The tool underwent a usability study with 8 parents whose infant survived the NICU stay and 5 neonatologists. Both parents and physicians thought the tool was easy to use, useful, and would help improve team communication. The next usability study will be with parents whose infant died while in the NICU, and then conduct a randomized prospective study with parents who have a sick infant admitted to the NICU.

Personalized health care and health information technology policy: an exploratory analysis

Personalized healthcare (PHC) is envisioned to enhance clinical practice decision-making using new genome-driven knowledge that tailors diagnosis, treatment, and prevention to the individual patient. In 2012, we conducted a focused environmental scan and informal interviews with fifteen experts to anticipate how PHC might impact health Information Technology (IT) policy in the United States. Findings indicatedthat PHC has a variable impact on current clinical practice, creates complex questions for providers, patients, and policy-makers, and will require a robust health IT infrastructure with advanced data architecture, clinical decision support, provider workflow tools, and re-use of clinical data for research. A number of health IT challenge areas were identified, along with five policy areas including: interoperable clinical decision support, standards for patient values and preferences, patient engagement, data transparency, and robust privacy and security.

Design and implementation of a shared treatment plan in a federated health information exchange

The poster presents the design and implementation of a shared treatment plan for providing unified views of medications for professionals and patients as a new added-value service on the regional healthcare network "e-toile". Strategies for integrating this service with other institutions infrastructures are also presented.

An integrated risk assessment tool for team-based periodontal disease management

Mounting evidence suggests a potential association of periodontal disease with systemic diseases such as diabetes, cardiovascular disease, cancer and stroke. The objective of this study is to develop an integrated risk assessment tool that displays a patients' risk for periodontal disease in the context of their systemic disease, social habits and oral health. Such a tool will be used by not just dental professionals but also by care providers who participate in the team-based care for chronic disease management. Displaying relationships between risk factors and its influence on the patient's general health could be a powerful educational and disease management tool for patients and clinicians. It may also improve the coordination of care provided by the provider-members of a chronic care team.

Evaluation of alerts for potassium-increasing drug-drug-interactions

Electronic alerts for preventing hyperkalaemia during potassium-increasing drug-drug-interactions (DDIs) are often overridden due to their low specificity. Treatments of 76,467 inpatients were retrospectively analysed to establish more specific alerts. Alerting concepts for identifying DDIs that induced hyperkalaemia (serum potassium ≥5.5 mEq/l were compared. The positive predictive value (PPV) of alerts was 2.9% if they were triggered at onset of each potassium-increasing DDI. The PPV increased to 5.1% if alerts at onset were suppressed for serum potassium levels of <4.0 mEq/l. The PPV rose to 24.2% with a novel approach, triggering alerts whenever an elevated potassium level of >4.8 mEq/l was detected at onset or during the entire DDI period. Thus, triggering DDI alerts based on periodically monitored potassium levels may improve specificity of alerts and thereby reduce alert fatigue.

Development of a SNOMED CT based national medication decision support system

Physicians often lack the time to familiarize themselves with the details of particular allergies or other drug restrictions. Clinical Decision Support (CDS), based on a structured terminology as SNOMED CT (SCT), can help physicians get an overview, by automatically alerting allergy, interactions and other important information. The centralized CDS platform based on SCT, controls Allergy, Interactions, Risk Situation Drugs and Max Dose restrictions by the help of databases developed for these specific purposes. The CDS will respond to automatic web service requests from the hospital or GP electronic medication system (EMS) during prescription, and return alerts and information. The CDS also contains a Physicians Preference Database where the physicians individually can set which kind of alerts they want to see. The result is clinically useful information physicians can use as a base for a more effective and safer treatment, without developing alert fatigue.

Noninvasive diagnosis of nonalcoholic steatohepatitis disease based on clinical decision support system

Non-alcoholic fatty liver disease (NAFLD) is a hepatic disease associated with metabolic syndrome. NAFLD covers a spectrum of liver disease from steatosis to non-alcoholic steatohepatitis (NASH) and cirrhosis. NASH is a disease evolving under the influence of various stimuli still poorly understood. In this paper we present new clinical decision support system (CDSS) for the diagnosis of NASH and the comparison of this system with machine learning algorithms.

Qualitative evaluation of the supporting system for diagnosis procedure combination code selection

In Japan, medical staff must select a diagnosis procedure combination (DPC) code for each inpatient upon admission. We report on the development and evaluation of a supporting system for DPC code selection. This system, based on a machine learning method developed by Okamoto et al., makes DPC code suggestions that are derived from medical practice information pertaining to inpatients. The use of the suggestions helps medical staff select an appropriate DPC code for each inpatient. We asked health information management professionals to evaluate the system and to compare the suggested DPC codes with those selected by doctors. They reported that the system was generally useful and that using this system they could find some cases of hospitalized patients whose DPC codes needed correction. However, they also determined the precision of the system needs improvement.

The development of a decision support system of vocational counseling for people with disabilities

After a disability occurs, vocational rehabilitation is essential for promoting return to society and improving quality of life. To facilitate vocational rehabilitation, an effective counseling by human expert is essential. However, the number of the experts is not many. Thus, people with disabilities (PWD) have had difficulty in having proper vocational consultation service. To mitigate this problem, this study aims at developing a decision support system (DSS) to recommend appropriate jobs to PWD based on their characteristics. For doing this, the experts in disabilities, occupational research, and information systems participated in building the logic of the system. The DSS for scientific and quantitative vocational counseling enables job counselors to recommend appropriate occupations considering PWDs' characteristics.

Experts speak: advice from key informants to small, rural hospitals on implementing the electronic health record system

The US government has allocated $30 billion dollars to implement Electronic Health Records (EHRs) in hospitals and provider practices through a policy called Meaningful Use. Small, rural hospitals, particularly those designated as Critical Access Hospitals (CAHs), comprising nearly a quarter of US hospitals, had not implemented EHRs before. Little is known on implementation in this setting. We interviewed a spectrum of 31 experts in the domain. The interviews were then analyzed qualitatively to ascertain the expert recommendations. Nineteen themes emerged. The pool of experts included staff from CAHs that had recently implemented EHRs. We were able to compare their answers with those of other experts and make recommendations for stakeholders. CAH peer experts focused less on issues such as physician buy-in, communication, and the EHR team. None of them indicated concern or focus on clinical decision support systems, leadership, or governance. They were especially concerned with system selection, technology, preparatory work and a need to know more about workflow and optimization. These differences were explained by the size and nature of these small hospitals.

Review article: the evolving role of information technology in perioperative patient safety

PURPOSE

The adoption of new technologies in medicine is frequently met with both enthusiasm and resistance. The universal adoption of health information technology (IT) and anesthesia information management systems (AIMS) remains low despite the potential benefits. Electronic medical records, and hence AIMS, are at the intersection of patient safety. This article highlights advantages and barriers to adoption and implementation of IT in general and AIMS in particular, with a focus on clinical decision support systems (CDSS) and computerized physician order entry (CPOE) as hallmarks that may lead to improvement in patient safety and quality in the perioperative setting.

PRINCIPAL FINDINGS

The advantages of health IT and AIMS include improved legibility of documentation; the ability to integrate new scientific evidence into practice; enhanced management and exchange of complex health information; the ability to standardize order sets, incorporate computerized physician order entry, and provide clinical decision support; and the ability to capture data for management, research, and quality monitoring and reporting. While not foolproof, AIMS have been shown to improve safety, quality, and patient outcomes. Barriers to the adoption of health IT and AIMS include costs, lack of truly interoperable AIMS components in health-system IT solutions, and lack of clinician involvement in implementation, planning, design, and installation of many IT or AIMS products.

CONCLUSIONS

Health IT and AIMS are at the intersection of patient safety and technology. Anesthesiologists are perfectly positioned to be the physician leaders of adoption, design, implementation, and integration, not only for AIMS but also for health-system IT solutions in general.

[From library to clinical decision support systems: access of general practitioner to quality information]

Since 2003, the following tools have been implemented in Belgium for improving the access of general practioners to the EBM literature: the Digital Library for Health and the evidence-linker of the CEBAM, the portal EBMPracticeNet.be and the multidimensional electronic clinical decision support EBMeDS. The aim of this article is to show the progress achieved in the information dissemination toward the belgian general practioners, particularly the access from the electronic health record. From the literature published these last years, the opportunities cited by the users are for using EBM and the strong willingness for using these literature access in the future; the limits are the medical data coding, the irrelevance of the search results, the alerts fatigue induced by EBMeDS. The achievements done and planned for the new EBMPracticeNet guidelines portal and the EBMeDS system are explained in the aim of informing belgian healthcare professionals. These projects are claiming for lauching a participatory process in the production and dissemination of EBM information. The discussion is focused on the belgian healthcare system advantages, the solutions for a reasonable implementation of these projects and for increasing the place of an evidence-based information in the healthcare decision process. Finally the input of these projects to the continuing medical education and to the healthcare quality are discussed, in a context of multifactorial interaction healthcare design (complexity design).

Barriers and facilitators to implementing Decision Boxes in primary healthcare teams to facilitate shared decisionmaking: a study protocol

BACKGROUND

Decision Boxes are summaries of the most important benefits and harms of health interventions provided to clinicians before they meet the patient, to prepare them to help patients make informed and value-based decisions. Our objective is to explore the barriers and facilitators to using Decision Boxes in clinical practice, more precisely factors stemming from (1) the Decision Boxes themselves, (2) the primary healthcare team (PHT), and (3) the primary care practice environment.

METHODS/DESIGN

A two-phase mixed methods study will be conducted. Eight Decision Boxes relevant to primary care, and written in both English and in French, will be hosted on a website together with a tutorial to introduce the Decision Box. The Decision Boxes will be delivered as weekly emails over a span of eight weeks to clinicians of PHTs (family physicians, residents and nurses) in five primary care clinics located across two Canadian provinces. Using a web-questionnaire, clinicians will rate each Decision Box with the Information Assessment Method (cognitive impacts, relevance, usefulness, expected benefits) and with a questionnaire based on the Theory of Planned Behavior to study the determinants of clinicians' intention to use what they learned from that Decision Box in their patient encounter (attitude, social norm, perceived behavioral control). Web-log data will be used to monitor clinicians' access to the website. Following the 8-week intervention, we will conduct semi-structured group interviews with clinicians and individual interviews with clinic administrators to explore contextual factors influencing the use of the Decision Boxes. Data collected from questionnaires, focus groups and individual interviews will be combined to identify factors potentially influencing implementation of Decision Boxes in clinical practice by clinicians of PHTs.

CONCLUSIONS

This project will allow tailoring of Decision Boxes and their delivery to overcome the specific barriers identified by clinicians of PHTs to improve the implementation of shared decision making in this setting.

Clinical software development for the Web: lessons learned from the BOADICEA project

BACKGROUND

In the past 20 years, society has witnessed the following landmark scientific advances: (i) the sequencing of the human genome, (ii) the distribution of software by the open source movement, and (iii) the invention of the World Wide Web. Together, these advances have provided a new impetus for clinical software development: developers now translate the products of human genomic research into clinical software tools; they use open-source programs to build them; and they use the Web to deliver them. Whilst this open-source component-based approach has undoubtedly made clinical software development easier, clinical software projects are still hampered by problems that traditionally accompany the software process. This study describes the development of the BOADICEA Web Application, a computer program used by clinical geneticists to assess risks to patients with a family history of breast and ovarian cancer. The key challenge of the BOADICEA Web Application project was to deliver a program that was safe, secure and easy for healthcare professionals to use. We focus on the software process, problems faced, and lessons learned. Our key objectives are: (i) to highlight key clinical software development issues; (ii) to demonstrate how software engineering tools and techniques can facilitate clinical software development for the benefit of individuals who lack software engineering expertise; and (iii) to provide a clinical software development case report that can be used as a basis for discussion at the start of future projects.

RESULTS

We developed the BOADICEA Web Application using an evolutionary software process. Our approach to Web implementation was conservative and we used conventional software engineering tools and techniques. The principal software development activities were: requirements, design, implementation, testing, documentation and maintenance. The BOADICEA Web Application has now been widely adopted by clinical geneticists and researchers. BOADICEA Web Application version 1 was released for general use in November 2007. By May 2010, we had > 1200 registered users based in the UK, USA, Canada, South America, Europe, Africa, Middle East, SE Asia, Australia and New Zealand.

CONCLUSIONS

We found that an evolutionary software process was effective when we developed the BOADICEA Web Application. The key clinical software development issues identified during the BOADICEA Web Application project were: software reliability, Web security, clinical data protection and user feedback.

Enabling health care decisionmaking through clinical decision support and knowledge management

OBJECTIVES

To catalogue study designs used to assess the clinical effectiveness of CDSSs and KMSs, to identify features that impact the success of CDSSs/KMSs, to document the impact of CDSSs/KMSs on outcomes, and to identify knowledge types that can be integrated into CDSSs/KMSs.

DATA SOURCES

MEDLINE(®), CINAHL(®), PsycINFO(®), and Web of Science(®).

REVIEW METHODS

We included studies published in English from January 1976 through December 2010. After screening titles and abstracts, full-text versions of articles were reviewed by two independent reviewers. Included articles were abstracted to evidence tables by two reviewers. Meta-analyses were performed for seven domains in which sufficient studies with common outcomes were included.

RESULTS

We identified 15,176 articles, from which 323 articles describing 311 unique studies including 160 reports on 148 randomized control trials (RCTs) were selected for inclusion. RCTs comprised 47.5 percent of the comparative studies on CDSSs/KMSs. Both commercially and locally developed CDSSs effectively improved health care process measures related to performing preventive services (n = 25; OR 1.42, 95% confidence interval [CI] 1.27 to 1.58), ordering clinical studies (n = 20; OR 1.72, 95% CI 1.47 to 2.00), and prescribing therapies (n = 46; OR 1.57, 95% CI 1.35 to 1.82). Fourteen CDSS/KMS features were assessed for correlation with success of CDSSs/KMSs across all endpoints. Meta-analyses identified six new success features: Integration with charting or order entry system. Promotion of action rather than inaction. No need for additional clinician data entry. Justification of decision support via research evidence. Local user involvement. Provision of decision support results to patients as well as providers. Three previously identified success features were confirmed: Automatic provision of decision support as part of clinician workflow. Provision of decision support at time and location of decisionmaking. Provision of a recommendation, not just an assessment. Only 29 (19.6%) RCTs assessed the impact of CDSSs on clinical outcomes, 22 (14.9%) assessed costs, and 3 assessed KMSs on any outcomes. The primary source of knowledge used in CDSSs was derived from structured care protocols.

CONCLUSIONS

Strong evidence shows that CDSSs/KMSs are effective in improving health care process measures across diverse settings using both commercially and locally developed systems. Evidence for the effectiveness of CDSSs on clinical outcomes and costs and KMSs on any outcomes is minimal. Nine features of CDSSs/KMSs that correlate with a successful impact of clinical decision support have been newly identified or confirmed.

Electronic decision protocols for ART patient triaging to expand access to HIV treatment in South Africa: a cross sectional study for development and validation

BACKGROUND

The shortage of doctors and nurses, along with future expansion into rural clinics, will require that the majority of clinic visits by HIV infected patients on antiretroviral therapy (ART) are managed by non-doctors. The goal of this study was to develop and evaluate a screening protocol to determine which patients needed a full clinical assessment and which patients were stable enough to receive their medications without a doctor's consultation. For this study, we developed an electronic, handheld tool to guide non-physician counselors through screening questions.

METHODS

Patients visiting two ART clinics in South Africa for routine follow-up visits between March 2007 and April 2008 were included in our study. Each patient was screened by non-physician counselors using the handheld device and then received a full clinical assessment. Clinicians' report on whether full clinical assessment had been necessary was used as the gold standard for determining "required referral". Observations were randomly divided into two datasets--989 for developing a referral protocol and 200 for validating protocol performance.

RESULTS

A third of patients had at least one physical complaint, and 16% had five or more physical complaints. 38% of patients required referral for full clinical assessment. We identify a subset of questions which are 87% sensitive and 47% specific for recommended patient referral.

CONCLUSIONS

The final screening protocol is highly sensitive and could reduce burden on ART clinicians by 30%. The uptake and acceptance of the handheld tool to support implementation of the protocol was high. Further examination of the data reveals several important questions to include in future referral algorithms to improve sensitivity and specificity. Based on these results, we identify a refined algorithm to explore in future evaluations.

Evaluating the use of a computerized clinical decision support system for asthma by pediatric pulmonologists

PURPOSE

To investigate use of a new guideline-based, computerized clinical decision support (CCDS) system for asthma in a pediatric pulmonology clinic of a large academic medical center.

METHODS

We conducted a qualitative evaluation including review of electronic data, direct observation, and interviews with all nine pediatric pulmonologists in the clinic. Outcome measures included patterns of computer use in relation to patient care, and themes surrounding the relationship between asthma care and computer use.

RESULTS

The pediatric pulmonologists entered enough data to trigger the decision support system in 397/445 (89.2%) of all asthma visits from January 2009 to May 2009. However, interviews and direct observations revealed use of the decision support system was limited to documentation activities after clinic sessions ended. Reasons for delayed use reflected barriers common to general medical care and barriers specific to subspecialty care. Subspecialist-specific barriers included the perceived high complexity of patients, the impact of subject matter expertise on the types of decision support needed, and unique workflow concerns such as the need to create letters to referring physicians.

CONCLUSIONS

Pediatric pulmonologists demonstrated low use of a computerized decision support system for asthma care because of a combination of general and subspecialist-specific factors. Subspecialist-specific factors should not be underestimated when designing guideline-based, computerized decision support systems for the subspecialty setting.