Decision making based on quality-of-information a clinical guideline for chronic obstructive pulmonary disease scenario

Springer - Series Advances in Intelligent and Soft Computing, vol. 79In this work we intend to advance towards a computational model to hold up a Group Decision Support System for VirtualECare, a system aimed at sustaining online healthcare services, where Extended Logic Programs (ELP) will be used for knowledge re-presentation and reasoning. Under this scenario it is possible to evaluate the ELPs making in terms of the Quality-of-Information (QoI) that is assigned to them, along the several stages of the decision making process, which is given as a truth value in the interval 0…1, i.e., it is possible to provide a measure of the value of the QoI that supports the decision making process, an end in itself. It will be also considered the problem ofQoI evaluation in a multi-criteria decision setting, being the criteria to be fulfilled that of a Clinical Guideline (CG) for Chronic Obstructive Pulmonary Disease

Experience with decision support system and comfort with topic predict clinicians’ responses to alerts and reminders

Objective Clinicians at our institution typically respond to about half of the prompts they are given by the clinic’s computer decision support system (CDSS). We sought to examine factors associated with clinician response to CDSS prompts as part of a larger, ongoing quality improvement effort to optimize CDSS use. Methods We examined patient, prompt, and clinician characteristics associated with clinician response to decision support prompts from the Child Health Improvement through Computer Automation (CHICA) system. We asked pediatricians who were nonusers of CHICA to rate decision support topics as “easy” or “not easy” to discuss with patients and their guardians. We analyzed these ratings and data, from July 1, 2009 to January 29, 2013, utilizing a hierarchical regression model, to determine whether factors such as comfort with the prompt topic and the length of the user’s experience with CHICA contribute to user response rates. Results We examined 414 653 prompts from 22 260 patients. The length of time a clinician had been using CHICA was associated with an increase in their prompt response rate. Clinicians were more likely to respond to topics rated as “easy” to discuss. The position of the prompt on the page, clinician gender, and the patient’s age, race/ethnicity, and preferred language were also predictive of prompt response rate. Conclusion This study highlights several factors associated with clinician prompt response rates that could be generalized to other health information technology applications, including the clinician’s length of exposure to the CDSS, the prompt’s position on the page, and the clinician’s comfort with the prompt topic. Incorporating continuous quality improvement efforts when designing and implementing health information technology may ensure that its use is optimized

Computer decision support changes physician practice but not knowledge regarding autism spectrum disorders

Objective: To examine whether adding an autism module promoting adherence to clinical guidelines to an existing computer decision support system (CDSS) changed physician knowledge and self-reported clinical practice. Methods: The CHICA (Child Health Improvement through Computer Automation) system, a CDSS, was enhanced with a module to improve management of autism in 2 of the 4 community pediatric clinics using the system. We examined the knowledge and beliefs of pediatric users using cross-sectional surveys administered at 3 time points (baseline, 12 months and 24 months post-implementation) between November 2010 and January 2013. Surveys measured knowledge, beliefs and self-reported practice patterns related to autism. Results: A total of 45, 39, and 42 pediatricians responded at each time point, respectively, a 95-100% response rate. Respondents’ knowledge of autism and perception of role for diagnosis did not vary between control and intervention groups either at baseline or any of the two post-intervention time points. At baseline, there was no difference between these groups in rates in the routine use of parent-rated screening instruments for autism. However, by 12 and 24 months post-implementation there was a significant difference between intervention and control clinics in terms of the intervention clinics consistently screening eligible patients with a validated autism tool. Physicians at all clinics reported ongoing challenges to community resources for further work-up and treatment related to autism. Conclusions: A CDSS module to improve primary care management of ASD in pediatric practice led to significant improvements in physician-reported use of validated screening tools to screen for ASDs. However it did not lead to corresponding changes in physician knowledge or attitudes

Pediatric decision support using adapted Arden Syntax

BACKGROUND: Pediatric guidelines based care is often overlooked because of the constraints of a typical office visit and the sheer number of guidelines that may exist for a patient's visit. In response to this problem, in 2004 we developed a pediatric computer based clinical decision support system using Arden Syntax medical logic modules (MLM). METHODS: The Child Health Improvement through Computer Automation system (CHICA) screens patient families in the waiting room and alerts the physician in the exam room. Here we describe adaptation of Arden Syntax to support production and consumption of patient specific tailored documents for every clinical encounter in CHICA and describe the experiments that demonstrate the effectiveness of this system. RESULTS: As of this writing CHICA has served over 44,000 patients at 7 pediatric clinics in our healthcare system in the last decade and its MLMs have been fired 6182,700 times in "produce" and 5334,021 times in "consume" mode. It has run continuously for over 10 years and has been used by 755 physicians, residents, fellows, nurse practitioners, nurses and clinical staff. There are 429 MLMs implemented in CHICA, using the Arden Syntax standard. Studies of CHICA's effectiveness include several published randomized controlled trials. CONCLUSIONS: Our results show that the Arden Syntax standard provided us with an effective way to represent pediatric guidelines for use in routine care. We only required minor modifications to the standard to support our clinical workflow. Additionally, Arden Syntax implementation in CHICA facilitated the study of many pediatric guidelines in real clinical environments

Secondhand smoke exposure, parental depressive symptoms and preschool behavioral outcomes

Little is known about the association of secondhand smoke (SHS) exposure and behavioral conditions among preschoolers. A cross-sectional analysis was used to examine billing and pharmacy claims from November 2004 to June 2012 linked to medical encounter-level data for 2,441 children from four pediatric community health clinics. Exposure to SHS was associated with attention deficit-hyperactivity disorder/ADHD and disruptive behavior disorder/DBD after adjusting for potential confounding factors. Assessment of exposure to SHS and parental depressive symptoms in early childhood may increase providers' ability to identify children at higher risk of behavioral issues and provide intervention at the earliest stages

Follow-up of Mothers with Suspected Postpartum Depression from Pediatrics Clinics

Purpose Pediatric providers are increasingly screening for postpartum depression (PD), yet, it is unknown how often mothers comply with recommendations to seek treatment. The objectives were to describe the rate at which mothers with suspected PD seek treatment and explore factors that predict help-seeking behavior. Design and methods Mothers were recruited from four pediatric clinics after identification using the Child Health Improvement through Computer Automation (CHICA) system. Mothers with a positive screen were invited to participate in a telephone interview between January 2012 and December 2014. Mothers reported if they sought treatment or called a community resource. Results 73 of 133 eligible mothers participated (55% response rate). Fifty women recalled a recommendation to seek help. Only 43.8% (32/73) made a follow-up appointment with an adult provider and even fewer kept the appointment. Conclusion A majority of mothers suspected of having PD recalled a referral for further intervention; yet, less than half took action. Further investigation of barriers of help-seeking behavior is warranted

A PROBABILISTIC APPROACH TO DATA INTEGRATION IN BIOMEDICAL RESEARCH: THE IsBIG EXPERIMENTS

Indiana University-Purdue University Indianapolis (IUPUI)Biomedical research has produced vast amounts of new information in the last decade but has been slow to find its use in clinical applications. Data from disparate sources such as genetic studies and summary data from published literature have been amassed, but there is a significant gap, primarily due to a lack of normative methods, in combining such information for inference and knowledge discovery. In this research using Bayesian Networks (BN), a probabilistic framework is built to address this gap. BN are a relatively new method of representing uncertain relationships among variables using probabilities and graph theory. Despite their computational complexity of inference, BN represent domain knowledge concisely. In this work, strategies using BN have been developed to incorporate a range of available information from both raw data sources and statistical and summary measures in a coherent framework. As an example of this framework, a prototype model (In-silico Bayesian Integration of GWAS or IsBIG) has been developed. IsBIG integrates summary and statistical measures from the NIH catalog of genome wide association studies (GWAS) and the database of human genome variations from the international HapMap project. IsBIG produces a map of disease to disease associations as inferred by genetic linkages in the population. Quantitative evaluation of the IsBIG model shows correlation with empiric results from our Electronic Medical Record (EMR) – The Regenstrief Medical Record System (RMRS). Only a small fraction of disease to disease associations in the population can be explained by the linking of a genetic variation to a disease association as studied in the GWAS. None the less, the model appears to have found novel associations among some diseases that are not described in the literature but are confirmed in our EMR. Thus, in conclusion, our results demonstrate the potential use of a probabilistic modeling approach for combining data from disparate sources for inference and knowledge discovery purposes in biomedical research