Vibha Anand

Automated Primary Care Screening in Pediatric Waiting Rooms

BACKGROUND AND OBJECTIVE: Implementing US Preventive Services Task Force and American Academy of Pediatrics preventive service guidelines within the short duration of a visit is difficult because identifying which of a large number of guidelines apply to a particular patient is impractical. Clinical decision support system integrated with electronic medical records offer a good strategy for implementing screening in waiting rooms. Our objective was to determine rates of positive risk screens during typical well-care visits among children and adolescents in a primary care setting. METHODS: Child Health Improvement through Computer Automation (CHICA) is a pediatric clinical decision support system developed by our research group. CHICA encodes clinical guidelines as medical logic modules to generate scanable paper forms: the patient screening form to collect structured data from patient families in the waiting room and the physician worksheet to provide physician assessments at each visit. By using visit as a unit of analysis from CHICA’s database, we have determined positive risk screen rates in our population. RESULTS: From a cohort of 16 963 patients, 408 601 questions were asked in 31 843 visits. Of the questions asked, 362 363 (89%) had a response. Of those, 39 176 (11%) identified positive risk screens in both the younger children and the adolescent age groups. CONCLUSIONS: By automating the process of screening and alerting the physician to those who screened positive, we have significantly decreased the burden of identifying relevant guidelines and screening of patient families in our clinics.

Targeted screening for pediatric conditions with the CHICA system

OBJECTIVE The Child Health Improvement through Computer Automation (CHICA) system is a decision-support and electronic-medical-record system for pediatric health maintenance and disease management. The purpose of this study was to explore CHICAs ability to screen patients for disorders that have validated screening criteria--specifically tuberculosis (TB) and iron-deficiency anemia. DESIGN Children between 0 and 11 years were randomized by the CHICA system. In the intervention group, parents were asked about TB and iron-deficiency risk, and physicians received a tailored prompt. In the control group, no screens were performed, and the physician received a generic prompt about these disorders. RESULTS 1123 participants were randomized to the control group and 1116 participants to the intervention group. Significantly more people reported positive risk factors for iron-deficiency anemia in the intervention group (17.5% vs 3.1%, OR 6.6, 95% CI 4.5 to 9.5). In general, far fewer parents reported risk factors for TB than for iron-deficiency anemia. Again, there were significantly higher detection rates of positive risk factors in the intervention group (1.8% vs 0.8%, OR 2.3, 95% CI 1.0 to 5.0). LIMITATIONS It is possible that there may be more positive screens without improving outcomes. However, the guidelines are based on studies that have evaluated the questions the authors used as sensitive and specific, and there is no reason to believe that parents misunderstood them. CONCLUSIONS Many screening tests are risk-based, not universal, leaving physicians to determine who should have a further workup. This can be a time-consuming process. The authors demonstrated that the CHICA system performs well in assessing risk automatically for TB and iron-deficiency anemia.

Use of a Computerized Decision Aid for Developmental Surveillance and Screening: A Randomized Clinical Trial

IMPORTANCE Developmental delays and disabilities are common in children. Research has indicated that intervention during the early years of a childs life has a positive effect on cognitive development, social skills and behavior, and subsequent school performance. OBJECTIVE To determine whether a computerized clinical decision support system is an effective approach to improve standardized developmental surveillance and screening (DSS) within primary care practices. DESIGN, SETTING, AND PARTICIPANTS In this cluster randomized clinical trial performed in 4 pediatric clinics from June 1, 2010, through December 31, 2012, children younger than 66 months seen for primary care were studied. INTERVENTIONS We compared surveillance and screening practices after adding a DSS module to an existing computer decision support system. MAIN OUTCOMES AND MEASURES The rates at which children were screened for developmental delay. RESULTS Medical records were reviewed for 360 children (180 each in the intervention and control groups) to compare rates of developmental screening at the 9-, 18-, or 30-month well-child care visits. The DSS module led to a significant increase in the percentage of patients screened with a standardized screening tool (85.0% vs 24.4%, P < .001). An additional 120 records (60 each in the intervention and control groups) were reviewed to examine surveillance rates at visits outside the screening windows. The DSS module led to a significant increase in the percentage of patients whose parents were assessed for concerns about their childs development (71.7% vs 41.7%, P = .04). CONCLUSIONS AND RELEVANCE Using a computerized clinical decision support system to automate the screening of children for developmental delay significantly increased the numbers of children screened at 9, 18, and 30 months of age. It also significantly improved surveillance at other visits. Moreover, it increased the number of children who ultimately were diagnosed as having developmental delay and who were referred for timely services at an earlier age. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01351077.

Understanding Why Clinicians Answer or Ignore Clinical Decision Support Prompts

INTRODUCTION The identification of key factors influencing responses to prompts and reminders within a computer decision support system (CDSS) has not been widely studied. The aim of this study was to evaluate why clinicians routinely answer certain prompts while others are ignored. METHODS We utilized data collected from a CDSS developed by our research group--the Child Health Improvement through Computer Automation (CHICA) system. The main outcome of interest was whether a clinician responded to a prompt. RESULTS This study found that, as expected, some clinics and physicians were more likely to address prompts than others. However, we also found clinicians are more likely to address prompts for younger patients and when the prompts address more serious issues. The most striking finding was that the position of a prompt was a significant predictor of the likelihood of the prompt being addressed, even after controlling for other factors. Prompts at the top of the page were significantly more likely to be answered than the ones on the bottom. CONCLUSIONS This study detailed a number of factors that are associated with physicians following clinical decision support prompts. This information could be instrumental in designing better interventions and more successful clinical decision support systems in the future.

Real Time Alert System: A Disease Management System Leveraging Health Information Exchange

Background Rates of preventive and disease management services can be improved by providing automated alerts and reminders to primary care providers (PCPs) using of health information technology (HIT) tools. Methods: Using Adaptive Turnaround Documents (ATAD), an existing Health Information Exchange (HIE) infrastructure and office fax machines, we developed a Real Time Alert (RTA) system. RTA is a computerized decision support system (CDSS) that is able to deliver alerts to PCPs statewide for recommended services around the time of the patient visit. RTA is also able to capture structured clinical data from providers using existing fax technology. In this study, we evaluate RTA’s performance for alerting PCPs when their patients with asthma have an emergency room visit anywhere in the state. Results: Our results show that RTA was successfully able to deliver “just in time” patient-relevant alerts to PCPs across the state. Furthermore, of those ATADs faxed back and automatically interpreted by the RTA system, 35% reported finding the provided information helpful. The PCPs who reported finding information helpful also reported making a phone call, sending a letter or seeing the patient for follow up care. Conclusions: We have successfully demonstrated the feasibility of electronically exchanging important patient related information with the PCPs statewide. This is despite a lack of a link with their electronic health records. We have shown that using our ATAD technology, a PCP can be notified quickly of an important event such as a patient’s asthma related emergency room admission so further follow up can happen in near real time.

The Last Mile: Using Fax Machines to Exchange Data between Clinicians and Public Health

There is overlap in a wide range of activities to support both public health and clinical care. Examples include immunization registries (IR), newborn screening (NBS), disease reporting, lead screening programs, and more. Health information exchanges create an opportunity to share data between the clinical and public health environments, providing decision support to clinicians and surveillance and tracking information to public health. We developed mechanisms to support two-way communication between clinicians in the Indiana Health information Exchange (IHIE) and the Indiana State Department of Health (ISDH). This paper describes challenges we faced and design decisions made to overcome them. We developed systems to help clinicians communicate with the ISDH IR and with the NBS program. Challenges included (1) a minority of clinicians who use electronic health records (EHR), (2) lack of universal patient identifiers, (3) identifying physicians responsible for newborns, and (4) designing around complex security policies and firewalls. To communicate electronically with clinicians without EHRs, we utilize their fax machines. Our rule-based decision support system generates tailored forms that are automatically faxed to clinicians. The forms include coded input fields that capture data for automatic transfer into the IHIE when they are faxed back. Because the same individuals have different identifiers, and newborns’ names change, it is challenging to match patients across systems. We use a stochastic matching algorithm to link records. We scan electronic clinical messages (HL7 format) coming into IHIE to find clinicians responsible for newborns. We have designed an architecture to link IHIE, ISDH, and our systems.

Tailoring Interface for Spanish Language: A Case Study with CHICA System

We developed a clinical decision support system (CDSS) --- Child Health Improvement through Computer Automation (CHICA) - to deliver patient specific guidance at the point of clinical care. CHICA captures structured data from families, physicians, and nursing, staff using a scannable paper user interface - Adaptive Turnaround Documents (ATD) while remaining sensitive to the workflow constraints of a busy outpatient pediatric practice. The system was deployed in November 2004 with an English language only user interface. In July 2005, we enhanced the user interface with a Spanish version of the pre-screening questionnaire to capture information from Spanish speaking families in our clinic. Subsequently, our results show an increase in rate of family responses to the pre-screening questionnaire by 36% (51% vs. 87%) in a four month time period before and after the Spanish interface deployment and up to 32% (51% vs. 83%) since November 2004. Furthermore, our results show that Spanish speaking families, on average, respond to the questionnaire more than English speaking families (85% vs. 49%). This paper describes the design, implementation challenges and our measure of success when trying to adapt a computer scannable paper interface to another language.

Translating genome wide association study results to associations among common diseases: In silico study with an electronic medical record

OBJECTIVE To develop a map of disease associations exclusively using two publicly available genetic sources: the catalog of single nucleotide polymorphisms (SNPs) from the HapMap, and the catalog of Genome Wide Association Studies (GWAS) from the NHGRI, and to evaluate it with a large, long-standing electronic medical record (EMR). METHODS A computational model, In Silico Bayesian Integration of GWAS (IsBIG), was developed to learn associations among diseases using a Bayesian network (BN) framework, using only genetic data. The IsBIG model (I-Model) was re-trained using data from our EMR (M-Model). Separately, another clinical model (C-Model) was learned from this training dataset. The I-Model was compared with both the M-Model and the C-Model for power to discriminate a disease given other diseases using a test dataset from our EMR. Area under receiver operator characteristics curve was used as a performance measure. Direct associations between diseases in the I-Model were also searched in the PubMed database and in classes of the Human Disease Network (HDN). RESULTS On the basis of genetic information alone, the I-Model linked a third of diseases from our EMR. When compared to the M-Model, the I-Model predicted diseases given other diseases with 94% specificity, 33% sensitivity, and 80% positive predictive value. The I-Model contained 117 direct associations between diseases. Of those associations, 20 (17%) were absent from the searches of the PubMed database; one of these was present in the C-Model. Of the direct associations in the I-Model, 7 (35%) were absent from disease classes of HDN. CONCLUSION Using only publicly available genetic sources we have mapped associations in GWAS to a human disease map using an in silico approach. Furthermore, we have validated this disease map using phenotypic data from our EMR. Models predicting disease associations on the basis of known genetic associations alone are specific but not sensitive. Genetic data, as it currently exists, can only explain a fraction of the risk of a disease. Our approach makes a quantitative statement about disease variation that can be explained in an EMR on the basis of genetic associations described in the GWAS.