Modelling domain knowledge using explicit conceptualization

Applications are characterized by the tasks and domains involved. Knowledge modeling can be divided into two conceptual subactivities: modeling the task and modeling the domain knowledge. An explicit conceptualization of the domain knowledge at the heart of its organization is discussed. A conceptualization is the objects presumed to exist and the relationships and functions among them. The annotations and the conceptualization guide the construction of applications and support flexible reasoning during problem solving. It also lets domain knowledge be reused

Effects of Computerized Decision Support Systems on Management of Atrial Fibrillation: A Scoping Review

Background: Potential role of computerized decision support system on management of atrial fibrillation is not well understood. Objectives: To systematically review studies that evaluate the effects of computerized decision support systems and decision aids on aspects pertaining to atrial fibrillation. Data sources: We searched Medline, Scopus and Cochrane database. Last date of search was 2016, January 10. Selection criteria: Computerized decision support systems that help manage atrial fibrillation and decision aids that provide useful knowledge for patients with atrial fibrillation and help them to self-care. Data collection and analysis: Two reviewers extracted data and summarized findings. Due to heterogeneity, meta-analysis was not feasible; mean differences of outcomes and confidence intervals for a difference between two Means were reported. Results: Seven eligible studies were included in the final review. There were one observational study without controls, three observational studies with controls, one Non-Randomized Controlled Trial and two Randomized Controlled Trials. The interventions were three decision aids that were used by patients and four computerized decision support systems. Main outcomes of studies were: stroke events and major bleeding (one article), Changing doctor-nurse behavior (three articles), Time in therapeutic International Normalized Ratio range (one article), decision conflict scale (two articles), patient knowledge and anxiety about stroke and bleeding (two articles). Conclusions: A computerized decision support system may decrease decision conflict and increase knowledge of patients with atrial fibrillation (AF) about risks of AF and AF treatments. Effect of computerized decision support system on outcomes such as changing doctor-nurse behavior, anxiety about stroke and bleeding and stroke events could not be shown.We need more studies to evaluate the role of computerized decision support system in patients with atrial fibrillation

A survey of attitudes, practices, and knowledge regarding drug-drug interactions among medical residents in Iran

Background When prescribing medications, physicians should recognize clinically relevant potential drug–drug interactions (DDIs). To improve medication safety, it is important to understand prescribers’ knowledge and opinions pertaining to DDIs. Objective To determine the current DDI information sources used by medical residents, their knowledge of DDIs, their opinions about performance feedback on co-prescription of interacting drugs. Setting Academic hospitals of Mashhad University of Medical Sciences (MUMS) in Iran. Methods A questionnaire containing questions regarding demographic and practice characteristics, DDI information sources, ability to recognize DDIs, and opinions about performance feedback was distributed to medical residents of 22 specialties in eight academic hospitals in Iran. We analyzed their perception pertaining to DDIs, their performance on classifying drug pairs, and we used a linear regression model to assess the association of potential determinants on their DDI knowledge. Main Outcome Measure prescribers’ knowledge and opinions pertaining to DDIs. Results The overall response rate and completion rate for 315 distributed questionnaires were 90% (n = 295) and 86% (n = 281), respectively. Among DDI information sources, books, software on mobile phone or tablet, and Internet were the most commonly-used references. Residents could correctly classify only 41% (5.7/14) of the drug pairs. The regression model showed no significant association between residents’ characteristics and their DDI knowledge. An overwhelming majority of the respondents (n = 268, 95.4%) wished to receive performance feedback on co-prescription of interacting drugs in their prescriptions. They mostly selected information technology-based tools (i.e. short text message and email) as their preferred method of receiving feedback. Conclusion Our findings indicate that prescribers may have poor ability to prevent clinically relevant potential DDI occurrence, and they perceive the need for performance feedback. These findings underline the importance of well-designed computerized alerting systems and delivering performance feedback to improve patient safety

Using HAQ-DI to estimate HUI-3 and EQ-5D utility values for patients with rheumatoid arthritis in Spain

AbstractBackground/ObjectiveUtility values are not usually assessed in clinical trials and do not allow cost-utility analysis to be performed with the data collected. The aim of this study was to derive relation functions so that Health Assessment Questionnaire – Disability Index (HAQ-DI) scores could be used to estimate Health Utilities Index - 3 (HUI-3) and EQ-5D utility values for patients with rheumatoid arthritis (RA).MethodsAn observational, cross-sectional, naturalistic, multicentre study was conducted. A total of 244 patients aged 18 years or older, with RA according to American College of Rheumatology diagnostic criteria, were recruited. Sociodemographic and clinical variables were recorded and patients completed three generic HRQoL questionnaires: the HAQ-DI, the HUI-3, and the EQ-5D. Two linear regression models were used to predict HUI-3 and EQ-5D utility values as functions of HAQ-DI scores, age, and gender.ResultsPatient mean age was 57.8 years old (standard deviation [SD], 13.3 years); 75.8% of the patients were women and 95.9% were white. Mean disease duration was 10.8 years (SD, 9 years). Patient distribution according to HAQ-DI severity was as follows: HAQ-DI < 0.5, 29%; 0.5 ≤ HAQ-DI < 1.1, 28%; 1.1 ≤ HAQ-DI < 1.6, 16%,1.6 ≤ HAQ-DI < 2.1, 15%; and HAQ-DI ≥ 2.1, 12%. HAQ-DI and EQ-5D mean scores were 1.02 (SD, 0.78) and 63.1 (SD, 20.3), respectively. Mean utility values for HUI-3 and time trade-off (TTO) were 0.75 (SD, 0.21) and 0.65 (SD, 0.3), respectively. The equations converting HAQ-DI scores to utilities were HUI-3 = 0.9527 – (0.2018 × HAQ-DI) +ε (R2=0.56), and TTO = 0.9567 – (0.309 × HAQ-DI) + ε (R2=0.54). Error distribution was non-normal. Age and gender were found to have no bearing on the utility functions.ConclusionsHAQ-DI scores can be used to estimate HUI-3 and EQ-5D utility values for patients with RA in data obtained from studies where utility values have not been collected

Identifying prognostic factors for clinical outcomes and costs in four high-volume surgical treatments using routinely collected hospital data

Identifying prognostic factors (PFs) is often costly and labor-intensive. Routinely collected hospital data provide opportunities to identify clinically relevant PFs and construct accurate prognostic models without additional data-collection costs. This multicenter (66 hospitals) study reports on associations various patient-level variables have with outcomes and costs. Outcomes were in-hospital mortality, intensive care unit (ICU) admission, length of stay, 30-day readmission, 30-day reintervention and in-hospital costs. Candidate PFs were age, sex, Elixhauser Comorbidity Score, prior hospitalizations, prior days spent in hospital, and socio-economic status. Included patients dealt with either colorectal carcinoma (CRC, n = 10,254), urinary bladder carcinoma (UBC, n = 17,385), acute percutaneous coronary intervention (aPCI, n = 25,818), or total knee arthroplasty (TKA, n = 39,214). Prior hospitalization significantly increased readmission risk in all treatments (OR between 2.15 and 25.50), whereas prior days spent in hospital decreased this risk (OR between 0.55 and 0.95). In CRC patients, women had lower risk of in-hospital mortality (OR 0.64), ICU admittance (OR 0.68) and 30-day reintervention (OR 0.70). Prior hospitalization was the strongest PF for higher costs across all treatments (31–64% costs increase/hospitalization). Prognostic model performance (c-statistic) ranged 0.67–0.92, with Brier scores below 0.08. R-squared ranged from 0.06–0.19 for LoS and 0.19–0.38 for costs. Identified PFs should be considered as building blocks for treatment-specific prognostic models and information for monitoring patients after surgery. Researchers and clinicians might benefit from gaining a better insight into the drivers behind (costs) prognosis