Susan Dovey

Treatment of postmenopausal osteoporosis with calcitriol or calcium

Background and Methods. Osteoporosis is a common problem whose management is controversial. To evaluate the efficacy and safety of calcitriol (1,25-dihydroxyvitamin D3) in the treatment of postmenopausal osteoporosis, we conducted a three-year prospective, multicenter, single-blind study in 622 women who had one or more vertebral compression fractures. The women were randomly assigned to receive treatment with calcitriol (0.25 μg twice a day) or supplemental calcium (1 g of elemental calcium daily) for three years. New vertebral fractures were detected by means of lateral roentgenography of the spine each year, and calcium absorption was measured in 392 of the women. Results. The women who received calcitriol had a significant reduction in the rate of new vertebral fractures during the second and third years of treatment, as compared with the women who received calcium (second year, 9.3 vs. 25.0 fractures per 100 patient-years; third year, 9.9 vs. 31.5 fractures per 100 patient-years; P<0.001). This effec...

Learning from malpractice claims about negligent, adverse events in primary care in the United States

Background: The epidemiology, risks, and outcomes of errors in primary care are poorly understood. Malpractice claims brought for negligent adverse events offer a useful insight into errors in primary care. Methods: Physician Insurers Association of America malpractice claims data (1985–2000) were analyzed for proportions of negligent claims by primary care specialty, setting, severity, health condition, and attributed cause. We also calculated risks of a claim for condition-specific negligent events relative to the prevalence of those conditions in primary care. Results: Of 49 345 primary care claims, 26 126 (53%) were peer reviewed and 5921 (23%) were assessed as negligent; 68% of claims were for negligent events in outpatient settings. No single condition accounted for more than 5% of all negligent claims, but the underlying causes were more clustered with “diagnosis error” making up one third of claims. The ratios of condition-specific negligent event claims relative to the frequency of those conditions in primary care revealed a significantly disproportionate risk for a number of conditions (for example, appendicitis was 25 times more likely to generate a claim for negligence than breast cancer). Conclusions: Claims data identify conditions and processes where primary health care in the United States is prone to go awry. The burden of severe outcomes and death from malpractice claims made against primary care physicians was greater in primary care outpatient settings than in hospitals. Although these data enhance information about error related negligent events in primary care, particularly when combined with other primary care data, there are many operating limitations.

A String of Mistakes: The Importance of Cascade Analysis in Describing, Counting, and Preventing Medical Errors

BACKGROUND Notions about the most common errors in medicine currently rest on conjecture and weak epidemiologic evidence. We sought to determine whether cascade analysis is of value in clarifying the epidemiology and causes of errors and whether physician reports are sensitive to the impact of errors on patients. METHODS Eighteen US family physicians participating in a 6-country international study filed 75 anonymous error reports. The narratives were examined to identify the chain of events and the predominant proximal errors. We tabulated the consequences to patients, both reported by physicians and inferred by investigators. RESULTS A chain of errors was documented in 77% of incidents. Although 83% of the errors that ultimately occurred were mistakes in treatment or diagnosis, 2 of 3 were set in motion by errors in communication. Fully 80% of the errors that initiated cascades involved informational or personal miscommunication. Examples of informational miscommunication included communication breakdowns among colleagues and with patients (44%), misinformation in the medical record (21%), mishandling of patients’ requests and messages (18%), inaccessible medical records (12%), and inadequate reminder systems (5%). When asked whether the patient was harmed, physicians answered affirmatively in 43% of cases in which their narratives described harms. Psychological and emotional effects accounted for 17% of physician-reported consequences but 69% of investigator-inferred consequences. CONCLUSIONS Cascade analysis of physicians’ error reports is helpful in understanding the precipitant chain of events, but physicians provide incomplete information about how patients are affected. Miscommunication appears to play an important role in propagating diagnostic and treatment mistakes.

Computer support for determining drug dose: systematic review and meta-analysis

Abstract Objective: To review the effectiveness of computer support for determining optimum drug dose. Design: Systematic review of comparative studies where computers gave advice to clinicians on the most appropriate drug dose. Search methods used were standard for the Cochrane Collaboration on Effective Professional Practice. Subjects: Comparative studies conducted worldwide and published between 1966 and 1996. Main outcome measures: For qualitative review, relative percentage differences were calculated to compare effects of computer support in different settings. For quantitative data, effect sizes were calculated and combined in meta-analyses. Results: Eighteen studies met the inclusion criteria. The drugs studied were theophylline, warfarin, heparin, aminoglycosides, nitroprusside, lignocaine, oxytocin, fentanyl, and midazolam. The computer programs used individualised pharmacokinetic models to calculate the most appropriate dose. Meta-analysis of data from 671 patients showed higher blood concentrations of drug with computer support (effect size 0.69, 95% confidence interval 0.36 to 1.02) and reduced time to achieve therapeutic control (0.44, 0.17 to 0.71). The total dose of drug used was unchanged, and there were fewer unwanted effects of treatment. Five of six studies measuring outcomes of care showed benefit from computer assistance. Conclusions: This review suggests that using computers to determine the correct dose of certain drugs in acute hospital settings is beneficial. Computers may give doctors the confidence to use higher doses when necessary, adjusting the drug dose more accurately to individual patients. Further research is necessary to evaluate the benefits in general use.

Testing process errors and their harms and consequences reported from family medicine practices: a study of the American Academy of Family Physicians National Research Network

Context: Little is known about the types and outcomes of testing process errors that occur in primary care. Objective: To describe types, predictors and outcomes of testing errors reported by family physicians and office staff. Design: Events were reported anonymously. Each office completed a survey describing their testing processes prior to event reporting. Setting and participants: 243 clinicians and office staff of eight family medicine offices. Main outcome measures: Distribution of error types, associations with potential predictors; predictors of harm and consequences of the errors. Results: Participants submitted 590 event reports with 966 testing process errors. Errors occurred in ordering tests (12.9%), implementing tests (17.9%), reporting results to clinicians (24.6%), clinicians responding to results (6.6%), notifying patient of results (6.8%), general administration (17.6%), communication (5.7%) and other categories (7.8%). Charting or filing errors accounted for 14.5% of errors. Significant associations (p<0.05) existed between error types and type of reporter (clinician or staff), number of labs used by the practice, absence of a results follow-up system and patients’ race/ethnicity. Adverse consequences included time lost and financial consequences (22%), delays in care (24%), pain/suffering (11%) and adverse clinical consequence (2%). Patients were unharmed in 54% of events; 18% resulted in some harm, and harm status was unknown for 28%. Using multilevel logistic regression analyses, adverse consequences or harm were more common in events that were clinician-reported, involved patients aged 45–64 years and involved test implementation errors. Minority patients were more likely than white, non-Hispanic patients to suffer adverse consequences or harm. Conclusions: Errors occur throughout the testing process, most commonly involving test implementation and reporting results to clinicians. While significant physical harm was rare, adverse consequences for patients were common. The higher prevalence of harm and adverse consequences for minority patients is a troubling disparity needing further investigation.

Patient Error: A Preliminary Taxonomy

PURPOSE Current research on errors in health care focuses almost exclusively on system and clinician error. It tends to exclude how patients may create errors that influence their health. We aimed to identify the types of errors that patients can contribute and help manage, especially in primary care. METHODS Eleven nominal group interviews of patients and primary health care professionals were held in Auckland, New Zealand, during late 2007. Group members reported and helped to classify types of potential error by patients. We synthesized the ideas that emerged from the nominal groups into a taxonomy of patient error. RESULTS Our taxonomy is a 3-level system encompassing 70 potential types of patient error. The first level classifies 8 categories of error into 2 main groups: action errors and mental errors. The action errors, which result in part or whole from patient behavior, are attendance errors, assertion errors, and adherence errors. The mental errors, which are errors in patient thought processes, comprise memory errors, mindfulness errors, misjudgments, and—more distally—knowledge deficits and attitudes not conducive to health. CONCLUSION The taxonomy is an early attempt to understand and recognize how patients may err and what clinicians should aim to influence so they can help patients act safely. This approach begins to balance perspectives on error but requires further research. There is a need to move beyond seeing patient, clinician, and system errors as separate categories of error. An important next step may be research that attempts to understand how patients, clinicians, and systems interact to cocreate and reduce errors.

Variation in the ecology of medical care.

BACKGROUND We wanted to quantify how the location in which medical care is delivered in the United States varies with the sociodemographic characteristics and health care arrangements of the individual person. Methods Data from the 1996 Medical Expenditures Panel Survey (MEPS) were used to estimate the number of persons per 1,000 per month in 1996 who had at least 1 contact with physicians’ offices, hospital outpatient departments, or emergency departments, hospitals, or home care. These data were stratified by age, sex, race, ethnicity, household income, education of head of household, residence in or out of metropolitan statistical areas, having health insurance, and having a usual source of care. Results Physicians’ offices were overwhelmingly the most common site of health care for all subgroups studied. Lacking a usual source of care was the only variable independently associated with a decreased likelihood of care in all 5 settings, and lack of insurance was associated with lower rates of care in all settings but emergency departments. Generally, more complicated patterns emerged for most sociodemographic characteristics. The combination of having a usual source of care and health insurance was especially related to higher rates of care in all settings except the emergency department. Conclusion Frequency and location of health care delivery varies substantially with sociodemographic characteristics, insurance, and having a usual source of care. Understanding this variation can inform public consideration of policy related to access to care.

Variation in participation in health care settings associated with race and ethnicity

OBJECTIVE: To use the ecology model of health care to contrast participation of black, non-Hispanics (blacks); white, non-Hispanics (whites); and Hispanics of any race (Hispanics) in 5 health care settings and determine whether disparities between those individuals exist among places where they receive care.DESIGN: 1996 Medical Expenditure Panel Survey data were used to estimate the number of black, white, and Hispanic people per 1,000 receiving health care in each setting.SETTING: Physicians’ offices, outpatient clinics, hospital emergency departments, hospitals, and people’s homes.MAIN MEASUREMENT: Number of people per 1,000 per month who had at least one contact in a health care setting.RESULTS: Fewer blacks and Hispanics than whites received care in physicians’ offices (154 vs 155 vs 244 per 1,000 per month, respectively) and outpatient clinics (15 vs 12 vs 24 per 1,000 per month, respectively). There were no significant differences in proportions hospitalized or receiving care in emergency departments. Fewer Hispanics than blacks or whites received home health care services (7 vs 14 vs 14 per 1,000 per month, respectively). After controlling for 7 variables, blacks and Hispanics were less likely than whites to receive care in physicians’ offices (odds ratio [OR], 0.65, 95% confidence interval [CI], 0.60 to 0.69 for blacks and OR, 0.79, 95% CI, 0.73 to 0.85 for Hispanics), outpatient clinics (OR, 0.73, 95% CI, 0.60 to 0.90 for blacks and OR, 0.71, 95% CI, 0.58 to 0.88 for Hispanics), and hospital emergency departments (OR, 0.80, 95% CI, 0.69 to 0.94 for blacks and OR, 0.80, 95% CI, 0.68 to 0.93 for Hispanics) in a typical month. The groups did not differ in the likelihood of receiving care in the hospital or at home.CONCLUSIONS: Fewer blacks and Hispanics than whites received health care in physicians’ offices, outpatient clinics, and emergency departments in contrast to hospitals and home care. Research and programs aimed at reducing disparities in receipt of care specifically in the outpatient setting may have an important role in the quest to reduce racial and ethnic disparities in health.

Learning from different lenses: Reports of medical errors in primary care by clinicians, staff, and patients - A project of the American academy of family physicians national research network

Objectives: To test whether family doctors, office staff, and patients will report medical errors and to investigate differences in how and what they report. Methods: Clinicians, staff, and patients in 10 family medicine clinics of the American Academy of Family Physicians National Research Network representing a diversity of clinical and community settings were invited to report errors they observed. They were asked to report routinely during 10 weeks and to report every error on 5 specific days. They submitted anonymous reports via a Web site, paper forms, and a voice-activated phone system. Results: Four hundred one clinicians and staff reported 935 errors within 717 events, 37% (265) of which came from the 5 intensive reporting days and 61% (440) from routine reports. Staff made 384 (53%) reports, and clinicians, 342 (47%) reports. Most (96%) errors reported were process errors, not related to knowledge or skill. Staff reported more errors in patient flow and communication; clinicians reported more medication and laboratory errors. Reports suggest that patients with complex health issues (31% versus 20%, P = 0.013) are vulnerable to more severe outcomes. Patients submitted 126 reports, 18 of which included errors. Conclusions: Clinicians and staff offer different and independently valuable lenses for understanding errors and their outcomes in primary care, but both predominantly reported process- or system-related errors. There is a clear need to find more effective ways to invite patients to report on errors or adverse events. These findings suggest that patient safety organizations authorized by recent legislation should invite reports from a variety of health care workers and staff.

Cluster randomized controlled trial of TIA electronic decision support in primary care

Objective: To test if TIA/stroke electronic decision support in primary care improves management. Methods: Multicenter, single-blind, parallel-group, cluster randomized, controlled trial comparing TIA/stroke electronic decision support guided management with usual care. Main outcomes were guideline adherence and 90-day stroke risk. Secondary outcomes were cerebrovascular/vascular/death/adverse events, cost, and user feedback. Main analysis was logistic regression with a normal random effect for clusters using a generalized linear mixed model. Results: Twenty-nine clinics were randomized to intervention, 27 to control, recruiting 172 and 119 eligible patients. More intervention patients received guideline-adherent care (131/172; 76.2%) than control patients (49/119; 41.2%) (adjusted odds ratio [OR] 4.57; 95% confidence interval [CI] 2.39–8.71; p < 0.001). Ninety-day stroke occurred in 2/172 (1.2%) intervention and 5/119 (4.2%) control patients (OR 0.27; 95% CI 0.05–1.41; p = 0.098). Ninety-day TIA or stroke occurrence was lower in the intervention group, 4/172 (2.3%) compared to 10/119 (8.5%) control (adjusted OR 0.26; 95% CI 0.70–0.97; p = 0.045). Fewer vascular events/deaths occurred in intervention, 6/172 (3.5%), than in control patients, 14/119 (11.9%) (adjusted OR 0.27; 95% CI 0.09–0.78; p = 0.016). Treatment cost ratio of 0.65 (95% CI 0.47–0.91; p = 0.013) favored the intervention without increased adverse events. Clinician feedback was positive. Conclusion: Primary care use of the TIA/stroke electronic decision support tool improves guideline adherence, safely reduces treatment cost, achieves positive user feedback, and may reduce cerebrovascular and vascular event risk following TIA/stroke. Classification of evidence: This study provides Class II evidence that a primary care electronic decision support tool improves guideline adherence and might reduce 90-day stroke risk.