Validated risk rule using computerized data to identify males at high risk for fracture.

UNLABELLED: Absolute risk assessment is now the preferred approach to guide osteoporosis treatment decisions. Data collected passively during routine healthcare operations can be used to develop discriminative absolute risk assessment rules in male veterans. These rules could be used to develop computerized clinical decision support tools that might improve fracture prevention. INTRODUCTION: Absolute risk assessment is the preferred approach to guiding treatment decisions in osteoporosis. Current recommended risk stratification rules perform poorly in men, among whom osteoporosis is overlooked and undertreated. A potential solution lies in clinical decision support technology. The objective of this study was to determine whether data passively collected in routine healthcare operations could identify male veterans at highest risk with acceptable discrimination. METHODS: Using administrative and clinical databases for male veterans ≥50 years old who sought care in 2005-2006, we created risk stratification rules for hip and any major fracture. We identified variables related to known or theoretical risk factors and created prognostic models using Cox regression. We validated the rules and estimated optimism. We created risk scores from hazards ratios and used them to predict fractures with logistic regression. RESULTS: The predictive models had C-statistics of 0.81 for hip and 0.74 for any major fracture, suggesting good to acceptable discrimination. For hip fracture, the cut-point that maximized percentage classified correctly (accuracy) predicted 165 of 227 hip fractures (73%) and missed 62 (27%). All hip fractures in patients with prior fracture were identified and 67% in patients without. For any major fracture, the maximal-accuracy cut-point predicted 611 of 987 (62%) and missed 376 (38%); the rule predicted all 134 fractures in patients with prior fracture and 56% in patients without. CONCLUSION: Data collected passively in routine healthcare operations can identify male veterans at highest risk for fracture with discrimination that exceeds that reported for other methods applied in men.

Electronic health records and adverse drug events after patient transfer.

BACKGROUND: Our objective was to examine the frequencies of medication error and adverse drug events (ADEs) at the time of patient transfer in a system with an electronic health record (EHR) as compared with a system without an EHR. It was hypothesised that the frequencies of these events would be lower in the EHR system because of better information exchange across sites of care. METHODS: 469 patients transferred between seven nursing homes and three hospitals in New York and Connecticut between 1999 and 2005 were followed retrospectively. Two groups of patients were compared: US Veterans Affairs (VA) patients, with an EHR, and non-VA patients, without an EHR, on the following measures: (1) medication prescribing discrepancies at nursing home/hospital transfer, (2) high-risk medication discrepancies and (3) ADEs caused by medication discrepancies according to structured medical record review by pairs of physician and pharmacist raters. RESULTS: The overall incidence of ADE caused by medication discrepancies was 0.20 per hospitalisation episode. After controlling for demographic and clinical covariates, there were no significant differences between VA and non-VA groups in medication discrepancies (mean difference 0.02; 95% CI -0.81 to 0.85), high-risk medication discrepancies (-0.18; 95%CI -0.22 to 0.58) or occurrence of an ADE caused by a medication discrepancy (OR 0.96; 95% CI 0.18 to 5.01). CONCLUSIONS: There was no difference, with and without an EHR, in the occurrence of medication discrepancies or ADEs caused by medication discrepancies at the time of transfer between sites of care. Reducing such problems may require specialised computer tools to facilitate medication review.

Direct text entry in electronic progress notes. An evaluation of input errors.

OBJECTIVES: It is not uncommon that the introduction of a new technology fixes old problems while introducing new ones. The Veterans Administration recently implemented a comprehensive electronic medical record system (CPRS) to support provider order entry. Progress notes are entered directly by clinicians, primarily through keyboard input. Due to concerns that there may be significant, invisible disruptions to information flow, this study was conducted to formally examine the incidence and characteristics of input errors in the electronic patient record. METHODS: Sixty patient charts were randomly selected from all 2,301 inpatient admissions during a 5-month period. A panel of clinicians with informatics backgrounds developed the review criteria. After establishing inter-rater reliability, two raters independently reviewed 1,891 notes for copying, copying errors, inconsistent text, inappropriate object insertion and signature issues. RESULTS: Overall, 60% of patients reviewed had one or more input-related errors averaging 7.8 errors per patient. About 20% of notes showed evidence of copying, with an average of 1.01 error per copied note. Copying another clinician's note and making changes had the highest risk of error. Templating resulted in large amounts of blank spaces. Overall, MDs make more errors than other clinicians even after controlling for the number of notes. CONCLUSIONS: Moving towards a more progressive model for the electronic medical record, where actions are recorded only once, history and physical information is encoded for use later, and note generation is organized around problems, would greatly minimize the potential for error.

Developing a taxonomy for research in adverse drug events: potholes and signposts.

Computerized decision support and order entry shows great promise for reducing adverse drug events (ADEs). The evaluation of these solutions depends on a framework of definitions and classifications that is clear and practical. Unfortunately the literature does not always provide a clear path to defining and classifying adverse drug events. While not a systematic review, this paper uses examples from the literature to illustrate problems that investigators will confront as they develop a conceptual framework for their research. It also proposes a targeted taxonomy that can facilitate a clear and consistent approach to the research of ADEs and aid in the comparison to results of past and future studies. The taxonomy addresses the definition of ADE, types, seriousness, error, and causality.