Jonathan R. Nebeker

Risk Factors for Adverse Drug Events: A 10-Year Analysis

BACKGROUND Many adverse drug events (ADEs) are the result of known pharmacologic properties, and some result from medication errors. However, some are the result of patient-specific risk factors. OBJECTIVE To identify inpatient risk factors for ADEs. METHODS Conditional logistic regression was used to analyze all pharmacist-verified ADEs by therapeutic class of drugs and severity during a 10-year study period. All inpatients ≥18 years of age from a 520-bed tertiary teaching hospital were included. Each case patient was matched with up to 16 control patients. Odds ratios for patient factors associated with ADEs were calculated from different therapeutic classes of drugs. RESULTS Odds ratios for numerous risk factors were identified for 4376 ADEs and were found to vary depending on therapeutic classification. The risk factors for the different classifications were grouped by (1) patient characteristics—female (OR 1.5–1.7), age (0.7–0.9), weight (1.2–1.4), creatinine clearance (0.8–4.7), and number of comorbidities (1.1–12.6); (2) drug administration—dosage (1.2–3.7), administration route (1.4–149.9), and number of concomitant drugs (1.2–2.4); and (3) patient type—service (1.2–4.9), nursing division (1.5–3.8), and diagnosis-related group (1.5–5.7). CONCLUSIONS Some risk factors are consistent for all ADEs and across multiple therapeutic classes of drugs, while others are class specific. High-risk agents should be closely monitored based on patient characteristics (gender, age, weight, creatinine clearance, number of comorbidities) and drug administration (dosage, administration route, number of concomitant drugs).

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 clinicians 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.

A Cognitive Task Analysis of Information Management Strategies in a Computerized Provider Order Entry Environment

OBJECTIVE Computerized Provider Order Entry (CPOE) with electronic documentation, and computerized decision support dramatically changes the information environment of the practicing clinician. Prior work patterns based on paper, verbal exchange, and manual methods are replaced with automated, computerized, and potentially less flexible systems. The objective of this study is to explore the information management strategies that clinicians use in the process of adapting to a CPOE system using cognitive task analysis techniques. DESIGN Observation and semi-structured interviews were conducted with 88 primary-care clinicians at 10 Veterans Administration Medical Centers. MEASUREMENTS Interviews were taped, transcribed, and extensively analyzed to identify key information management goals, strategies, and tasks. Tasks were aggregated into groups, common components across tasks were clarified, and underlying goals and strategies identified. RESULTS Nearly half of the identified tasks were not fully supported by the available technology. Six core components of tasks were identified. Four meta-cognitive information management goals emerged: 1) Relevance Screening; 2) Ensuring Accuracy; 3) Minimizing memory load; and 4) Negotiating Responsibility. Strategies used to support these goals are presented. CONCLUSION Users develop a wide array of information management strategies that allow them to successfully adapt to new technology. Supporting the ability of users to develop adaptive strategies to support meta-cognitive goals is a key component of a successful system.

Prescribing discrepancies likely to cause adverse drug events after patient transfer

Background: Medication-prescribing discrepancies are used as a quality measure for patients transferred between sites of care. The objective of this study was to quantify the rate of adverse drug events (ADEs) caused by prescribing discrepancies and the discrimination of an index of high-risk transition drug prescribing. Methods: We examined medical records of patients transferred between seven nursing homes and three hospitals between 1999 and 2005 in New York and Connecticut for transfer-associated prescribing discrepancies. ADEs caused by discrepancies were determined by two clinician raters. We calculated the fraction of medication discrepancies that caused ADEs in each of 22 drug classes by calculating positive predictive values (PPVs). We calculated the discrimination of a count of high-risk drug discrepancies, selected from published lists of high-risk medications and using observed PPVs. Results: 208 patients were hospitalised 304 times. Overall, 65 of 1350 prescribing discrepancies caused ADEs, for a PPV of 0.048 (95% CI 0.037 to 0.061). PPVs by drug class ranged from 0 to 0.28. Drug classes with the highest PPVs were opioid analgesics, metronidazole, and non-opioid analgesics. Patients with 0, 1–2 and ⩾3 high-risk discrepancies had a 13%, 23% and 47% chance of experiencing a discrepancy-related ADE, respectively. Conclusions: Discrepancies in certain drug classes more often caused ADEs than other types of discrepancies in hospitalised nursing-home patients. Information about ADEs caused by medication discrepancies can be used to enhance measurement of care quality, identify high-risk patients and inform the development of decision-support tools at the time of patient transfer.

Accelerated Approval of Cancer Drugs: Improved Access to Therapeutic Breakthroughs or Early Release of Unsafe and Ineffective Drugs?

PURPOSE Accelerated approval (AA) was initiated by the US Food and Drug Administration (FDA) to shorten development times of drugs for serious medical illnesses. Sponsors must confirm efficacy in postapproval trials. Confronted with several drugs that received AA on the basis of phase II trials and for which confirmatory trials were incomplete, FDA officials have encouraged sponsors to design AA applications on the basis of interim analyses of phase III trials. METHODS We reviewed data on orphan drug status, development time, safety, and status of confirmatory trials of AAs and regular FDA approvals of new molecular entities (NMEs) for oncology indications since 1995. RESULTS Median development times for AA NMEs (n = 19 drugs) and regular-approval oncology NMEs (n = 32 drugs) were 7.3 and 7.2 years, respectively. Phase III trials supported efficacy for 75% of regular-approval versus 26% of AA NMEs and for 73% of non-orphan versus 45% of orphan drug approvals. AA accounted for 78% of approvals for oncology NMEs between 2001 and 2003 but accounted for 32% in more recent years. Among AA NMEs, confirmatory trials were nine-fold less likely to be completed for orphan drug versus non-orphan drug indications. Postapproval, black box warnings were added to labels for four oncology NMEs (17%) that had received AA and for two oncology NMEs (9%) that had received regular approval. CONCLUSION AA oncology NMEs are safe and effective, although development times are not accelerated. A return to endorsing phase II trial designs for AA for oncology NMEs, particularly for orphan drug indications, may facilitate timely FDA approval of novel cancer drugs.

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.

Dissemination of Information on Potentially Fatal Adverse Drug Reactions for Cancer Drugs From 2000 to 2002: First Results From the Research on Adverse Drug Events and Reports Project

PURPOSE To describe the clinical findings, occurrence rates, causality evidence, and dissemination media for serious cancer drug-associated adverse drug reactions (ADRs) reported in the postmarketing setting. METHODS ADRs were termed serious if they resulted in death or severe organ failure. ADR information for oncology drugs from package insert (PI) revisions, so-called Dear Doctor letters, and journal articles was evaluated to identify serious ADRs reported from 2000 to 2002. Timing and content of information disseminated was assessed. RESULTS Twenty-five serious ADRs associated with 22 oncology drugs were identified after approval. Approximately half of these serious ADRs are associated with drugs approved before 1995. ADRs were described in articles in medical journals (17 ADRs), PI revisions (18 ADRs), and Dear Doctor letters (12 ADRs). PI revisions occurred less than 1 year after peer-reviewed publication for four ADRs. These revisions often differed for similar ADRs that occurred with drugs of the same class. Five of the seven ADRs lacking PI changes occurred with off-label use, for which PI change is not recommended by US Food and Drug Administration (FDA) policy. No cancer drug was withdrawn from the market during the observation period. CONCLUSION Our findings demonstrate that serious ADRs may be discovered as long as 36 years after a drug receives FDA approval. This suggests a need for continued vigilance and efficient strategies for dissemination of information about ADRs associated with cancer drugs.

Effect of Admission Medication Reconciliation on Adverse Drug Events From Admission Medication Changes

M edication reconciliation, a process by which a health care provider obtains and documents a thorough medication history with specific attention to comparing current and previous medication use, has been a focus of major patient safety initiatives. Evaluations of medication reconciliation programs have reported factors associated with successful implementation and its effect on prescribing outcomes such as medication errors and potential adverse drug events but not its effect on actual adverse drug events (ADEs). The objective of this study was to estimate the effectiveness of inpatient medication reconciliation at the time of hospital admission on ADEs caused by admission prescribing changes.

Pharmacist- versus physician-initiated admission medication reconciliation: impact on adverse drug events.

BACKGROUND Medication reconciliation (MR) has proven to be a problematic task for many hospitals to accomplish. It is important to know the clinical impact of physician- versus pharmacist-initiated MR in the resource-limited hospital environment. METHODS This quasi-experimental study took place from December 2005 to February 2006 at an urban US Veterans Affairs hospital. MR was implemented on 2 similar general medical units: one received physician-initiated MR and the other received pharmacist-initiated MR. Adverse drug events (ADEs) and a 72-hour medication-prescribing risk score were ascertained by research pharmacists for all admitted patients by structured record review. Multivariable models were tested for intervention effect, accounting for quasi-experimental design and clustered observations, and were adjusted for patient and encounter covariates. RESULTS Pharmacists completed the MR process in 102 admissions and physicians completed the process in 116 admissions. In completing the MR process, pharmacists documented statistically more admission medication changes than physicians (3.6 vs 0.8; P < 0.001). The adjusted odds of an ADE caused by an admission prescribing change with pharmacist-initiated MR compared with a physician-initiated MR were 1.04 with a 95% CI of 0.53 to 2.0. The adjusted odds of an ADE caused by an admission prescribing change that was a prescribing error with pharmacist-initiated MR compared with a physician-initiated MR were 0.38 with a confidence interval of 0.14 to 1.05. No difference was observed in 72-hour prescribing risk score (coefficient = 0.10; 95% CI, -0.54 to 0.75). CONCLUSION MR performed by pharmacists versus physicians was more comprehensive and was followed by lower odds of ADEs from admission prescribing errors but with similar odds of all types of ADEs. Further research is warranted to examine how MR tasks may be optimally divided among clinicians and the mechanisms by which MR affects the likelihood of subsequent ADEs.

pSCANNER: patient-centered Scalable National Network for Effectiveness Research

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administrations 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.