William G. Adams

Effect of Computer Order Entry on Prevention of Serious Medication Errors in Hospitalized Children

OBJECTIVE. Although initial research suggests that computerized physician order entry reduces pediatric medication errors, no comprehensive error surveillance studies have evaluated the effect of computerized physician order entry on children. Our objective was to evaluate comprehensively the effect of computerized physician order entry on the rate of inpatient pediatric medication errors. METHODS. Using interrupted time-series regression analysis, we reviewed all charts, orders, and incident reports for 40 admissions per month to the NICU, PICU, and inpatient pediatric wards for 7 months before and 9 months after implementation of commercial computerized physician order entry in a general hospital. Nurse data extractors, who were unaware of study objectives, used an established error surveillance method to detect possible errors. Two physicians who were unaware of when the possible error occurred rated each possible error. RESULTS. In 627 pediatric admissions, with 12 672 medication orders written over 3234 patient-days, 156 medication errors were detected, including 70 nonintercepted serious medication errors (22/1000 patient-days). Twenty-three errors resulted in patient injury (7/1000 patient-days). In time-series analysis, there was a 7% decrease in level of the rates of nonintercepted serious medication errors. There was no change in the rate of injuries as a result of error after computerized physician order entry implementation. CONCLUSIONS. The rate of nonintercepted serious medication errors in this pediatric population was reduced by 7% after the introduction of a commercial computerized physician order entry system, much less than previously reported for adults, and there was no change in the rate of injuries as a result of error. Several human-machine interface problems, particularly surrounding selection and dosing of pediatric medications, were identified. Additional refinements could lead to greater effects on error rates.

Medication errors related to computerized order entry for children

OBJECTIVE. The objective of this study was to determine the frequency and types of pediatric medication errors attributable to design features of a computerized order entry system. METHODS. A total of 352 randomly selected, inpatient, pediatric admissions were reviewed retrospectively for identification of medication errors, 3 to 12 months after implementation of computerized order entry. Errors were identified and classified by using an established, comprehensive, active surveillance method. Errors attributable to the computer system were classified according to type. RESULTS. Among 6916 medication orders in 1930 patient-days, there were 104 pediatric medication errors, of which 71 were serious (37 serious medication errors per 1000 patient-days). Of all pediatric medication errors detected, 19% (7 serious and 13 with little potential for harm) were computer related. The rate of computer-related pediatric errors was 10 errors per 1000 patient-days, and the rate of serious computer-related pediatric errors was 3.6 errors per 1000 patient-days. The following 4 types of computer-related errors were identified: duplicate medication orders (same medication ordered twice in different concentrations of syrup, to work around computer constraints; 2 errors), drop-down menu selection errors (wrong selection from a drop-down box; 9 errors), keypad entry error (5 typed instead of 50; 1 error), and order set errors (orders selected from a pediatric order set that were not appropriate for the patient; 8 errors). In addition, 4 preventable adverse drug events in drug ordering occurred that were not considered computer-related but were not prevented by the computerized physician order entry system. CONCLUSIONS. Serious pediatric computer-related errors are uncommon (3.6 errors per 1000 patient-days), but computer systems can introduce some new pediatric medication errors that are not typically seen in a paper ordering system.

Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture.

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the

Prediction of hospitalization due to heart diseases by supervised learning methods

48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

Diabetes Mellitus Screening in Pediatric Primary Care

BACKGROUND In 2008, the United States spent

Concurrent Ulcerative Colitis and Neurofibromatosis Type 1: The Question of a Common Pathway

2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that nearly

Enhancing the Electronic Health Record to Increase Counseling and Quit-Line Referral for Parents Who Smoke

30.8 billion in hospital care cost during 2006 was potentially preventable, with heart diseases being responsible for about 31% of that amount. METHODS Our goal is to accurately and efficiently predict heart-related hospitalizations based on the available patient-specific medical history. To the best of our knowledge, the approaches we introduce are novel for this problem. The prediction of hospitalization is formulated as a supervised classification problem. We use de-identified Electronic Health Record (EHR) data from a large urban hospital in Boston to identify patients with heart diseases. Patients are labeled and randomly partitioned into a training and a test set. We apply five machine learning algorithms, namely Support Vector Machines (SVM), AdaBoost using trees as the weak learner, logistic regression, a naïve Bayes event classifier, and a variation of a Likelihood Ratio Test adapted to the specific problem. Each model is trained on the training set and then tested on the test set. RESULTS All five models show consistent results, which could, to some extent, indicate the limit of the achievable prediction accuracy. Our results show that with under 30% false alarm rate, the detection rate could be as high as 82%. These accuracy rates translate to a considerable amount of potential savings, if used in practice.

Evaluation of a web-based asthma self-management system: a randomised controlled pilot trial.

OBJECTIVE. The goal was to determine the rates of diabetes screening and the prevalence of screening abnormalities in overweight and nonoverweight individuals in an urban primary care clinic. METHODS. This study was a retrospective chart review conducted in a hospital-based urban primary care setting. Deidentified data for patients who were 10 to 19 years of age and had ≥1 BMI measurement between September 1, 2002, and September 1, 2004, were extracted from the hospital electronic health record. RESULTS. A total of 7710 patients met the study criteria. Patients were 73.0% black or Hispanic and 47.0% female; 42.0% of children exceeded normal weight, with 18.2% at risk for overweight and 23.8% overweight. On the basis of BMI, family history, and race, 8.7% of patients met American Diabetes Association criteria for type 2 diabetes mellitus screening, and 2452 screening tests were performed for 1642 patients. Female gender, older age group, and family history of diabetes were associated with screening. Increasing BMI percentile was associated with screening, exhibiting a dose-response relationship. Screening rates were significantly higher (45.4% vs 19.0%) for patients who met the American Diabetes Association criteria; however, less than one half of adolescents who should have been screened were screened. Abnormal glucose metabolism was seen for 9.2% of patients screened. CONCLUSIONS. This study shows that, although pediatricians are screening for diabetes mellitus, screening is not being conducted according to the American Diabetes Association consensus statement. Point-of-care delivery of consensus recommendations could increase provider awareness of current recommendations, possibly improving rates of systematic screening and subsequent identification of children with laboratory evidence of abnormal glucose metabolism.

Causality assessment of adverse events reported to the Vaccine Adverse Event Reporting System (VAERS).

Patients with neurofibromatosis type 1 (NF1) are prone to the development of gastrointestinal stromal tumors, which may present clinically with hematochezia, obstruction, or abdominal pain. These symptoms are also commonly associated with the presentation of ulcerative colitis (UC). Within the past 5 years, there have been 2 reports of concurrent NF1 and UC and a common pathophysiologic pathway involving mast cells has been postulated. We present the case of a 15-year-old boy with a known history of NF1 who presented with 3 months of hematochezia and loose stools. A colonoscopy revealed pancolitis and histology demonstrating acute cryptitis, focal crypt abscesses, and architectural distortion consistent with UC. Due to the paucity of reported cases, the findings of both diseases in the same individual could reasonably be discounted as coincidence. However, in light of increasing reports of concurrent NF1 and UC, advances in characterizing the microenvironment within neurofibromas, and recent findings regarding potential shared genetic susceptibility, it is increasingly possible that the proposed common pathway is accurate. Our case adds to the literature and underscores the need for further investigation.

Zinc Protoporphyrin and Iron Deficiency Screening: Trends and Therapeutic Response in an Urban Pediatric Center

OBJECTIVE To assess the impact of an electronic health record (EHR) modification and brief clinician training on tobacco smoke exposure (TSE) management in pediatric primary care. METHODS Within a teaching hospital-based, urban primary care setting, we modified the EHR to include TSE screening prompts, decision support, educational literature, and simplified referral to the state quit line (QuitWorks). A brief training was conducted for the 48 clinic physicians (34 residents and 14 attendings). We collected cross-sectional, independent, random samples of EHR data from well-child visits for children ≤12 years old seen 3 months before (2024 visits) and 3 months after (1895 visits) the intervention and pooled client data from QuitWorks to evaluate TSE screening, counseling, and quit-line referrals. A needs assessment questionnaire examined preintervention attitudes and practice around TSE management; follow-up questionnaires explored satisfaction and subjective changes in skills. RESULTS The baseline needs assessment revealed that although most clinicians agreed that it is appropriate for pediatricians to conduct TSE screening, counseling, and referral during well-child visits, only about half screened, 42% counseled, and 28% routinely offered to refer smoking parents. In pre-post analyses of 117 and 112 EHR-documented positive screens, the intervention was associated with a 16-fold greater likelihood of counseling among positive screens (adjusted odds ratio 16.12; 95% confidence interval 7.28, 35.68). Referrals to QuitWorks increased from 1 before to 31 after the intervention. CONCLUSIONS Implementation of EHR modifications and a brief training to support TSE management was associated with higher rates of counseling and quit-line referrals for parents who smoke.