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At least 127 records · Page 7Linked to original sources
PubMed2005

Communicating about medications during primary care outpatient visits: the role of electronic medical records.

OBJECTIVES: To assess the role of electronic medical records (EMR) in facilitating the content and process of patient-provider exchanges about medications during outpatient primary care visits. METHODS: Fifty encounters with six physicians using the EMR were videotaped, transcribed and content-analysed by applying conversation analysis and ethnomethodology techniques. The analysis focused on three aspects of medication communication: (1) process of care: practices by patients and physicians to implement medication decisions; (2) themes: medication topics that consistently emerge; and (3) names: ways patients and physicians refer to medications. In-depth analysis of 20 encounters examined the extent to which either or both parties initiated, expanded and concluded medication discussions. RESULTS: On average 21.2 (range: 8-35; SD=7.4) distinct exchanges per encounter were observed. Of those, 33% were related to medication. Of the 350 medication-related exchanges throughout the encounters, 56% were categorised as routine medication discussion such as ordering and/or refilling medications. Mailing issues were the next most common medication-related exchanges (10.6%), followed by partial adherence (8.9%), self-regulation (7.4%), alternative therapy/over-the-counter medication (6.6%), side effects (6%) and formulary issues (4.6%). Patients and providers used three ways to name medications: generic/scientific name (42%); physical description (39.7%) and brand name (18.3%). Forty-one percent of exchanges included initiation by one or both parties but no further discussion of the issue; 42% included initiation and expansion by both parties but not conclusion; only 17% of exchanges contained complete medication exchanges (initiation, expansion and conclusion) by both parties. CONCLUSIONS: EMR facilitated content and process of communication regarding medications during outpatient encounters, especially among patients taking multiple medications and patients who used physical descriptions to identify their medications. EMR use stimulated medication exchanges, leading to further expansion about the topic. However, fewer than one-fifth of exchanges ended with clear conclusions by both parties regarding prescribed medication regimens.

Adult↗
PubMed1997

Computerized medication administration records decrease medication occurrences.

Studies have demonstrated that medication errors occur at a number of locations in the continuum between ordering of drug therapy and administration of the medication. Computer management of patient medication profiles offers the opportunity to enhance communication between pharmacists and nurses, and to decrease medication errors and delays in delivery of therapy. A number of authors have postulated that computerization of medication profiles would enhance medication delivery accuracy and timeliness, but no study has demonstrated this improvement. We report the results of a retrospective analysis undertaken to assess the improvements resulting from sharing a computerized medication record. We used a broader definition of medication occurrences that includes the more traditional definition, and averted errors, delays in delivery of medications and information, and disagreements between pharmacy and nursing medication profiles. We compared medication occurrences reported through an existing internal system between two periods; the first when separate pharmacy and nursing medication records were used, and the second period when a shared medication record was used by pharmacy and nursing. Average medication occurrences per admission decreased from 0.1084 to 0.0658 (p < 0.01). Medication occurrences per dose decreased from 0.0005 to 0.0003 (p < 0.01). The use of a shared medication record by pharmacy and nursing led to a statistically significant decrease in medication occurrences. Information shared between the two professions allowed timely resolution of discrepancies in medication orders, leading to better execution of drug therapy, decreased medication occurrences, and increased efficiency.

Clinical Pharmacy Information Systems↗
PubMedDate not supplied

The accuracy of medication data in an outpatient electronic medical record.

OBJECTIVE: To measure the accuracy of medication records stored in the electronic medical record (EMR) of an outpatient geriatric center. The authors analyzed accuracy from the perspective of a clinician using the data and the perspective of a computer-based medical decision-support system (MDSS). DESIGN: Prospective cohort study. METHODS: The EMR at the geriatric center captures medication data both directly from clinicians and indirectly using encounter forms and data-entry clerks. During a scheduled office visit for medical care, the treating clinician determined whether the medication records for the patient were an accurate representation of the medications that the patient was actually taking. Using the available sources of information (the patient, the patient's vials, any caregivers, and the medical chart), the clinician determined whether the recorded data were correct, whether any data were missing, and the type and cause for each discrepancy found. RESULTS: At the geriatric center, 83% of medication records represented correctly the compound. dose, and schedule of a current medication; 91% represented correctly the compound. 0.37 current medications were missing per patient. The principal cause of errors was the patient (36.1% of errors), who misreported a medication at a previous visit or changed (stopped, started, or dose-adjusted) a medication between visits. The second most frequent cause of errors was failure to capture changes to medications made by outside clinicians, accounting for 25.9% of errors. Transcription errors were a relatively ucommon cause (8.2% of errors). When the accuracy of records from the center was analyzed from the perspective of a MDSS, 90% were correct for compound identity and 1.38 medications were missing or uncoded per patient. The cause of the additional errors of omission was a free-text "comments" field-which it is assumed would be unreadable by current MDSS applications-that was used by clinicians in 18% of records to record the identity of the medication. CONCLUSIONS: Medication records in an outpatient EMR may have significant levels of data error. Based on an analysis of correctable causes of error, the authors conclude that the most effective extension to the EMR studied would be to expand its scope to include all clinicians who can potentially change medications. Even with EMR extensions, however, ineradicable error due to patients and data entry will remain. Several implications of ineradicable error for MDSSs are discussed. The provision of a free-text "comments" field increased the accuracy of medication lists for clinician users at the expense of accuracy for a MDSS.

Aged↗
PubMed2005

Influence of computerised medication charts on medication errors in a hospital.

INTRODUCTION: In hospitals where computerised physician order entry systems will not be available in the near future, there is a need to explore other ways of reducing medication errors that occur in the drug ordering and delivery system. One of these ways is the use of a computerised medication chart that is updated daily. The aim of this study was to evaluate the frequency, types and potential clinical significance of drug prescription and administration errors by comparing a traditional medication distribution system (where the transcription of handwritten into printed medication orders takes 3-5 days and the transfer of medication orders was not complete) with the use of a computerised medication chart (which was updated daily by pharmacy assistants on the ward). METHODS: Data were collected during two 3-week periods, from a 32-bed internal medicine unit, before and after the introduction of the computerised medication charts. Prescribing errors were observed by evaluation of all new and changed medication orders and administration errors were detected by using the disguised-observation technique. RESULTS: For prescribing errors, a total of 611 prescriptions before and 598 prescriptions after the intervention were evaluated. The total prescription error rate (of medication orders with >or=1 error) was found to be significantly higher with the computerised charts when compared with the old system (50.0% [299 of 598] vs 20.3% [124 of 611], odds ratio [OR] 3.80 [95% CI 2.94, 4.90]). This increase was caused by an increase in administrative prescription errors with a low potential clinical significance (mainly omission of the prescriber's name and the prescription date). The error rate for errors with a potential clinical significance was found to be significantly lower because the prescription error 'duplicate therapy' was eliminated (3.4% with the traditional medication chart vs 0% with the computerised chart). For administration errors, a total of 1122 drugs before the intervention and 1175 drugs after the intervention was observed to be administered. The total administration error rate was found to be significantly lower after the intervention (6.1% [72 of 1175] vs 10.5% [118 of 1122], OR 0.61 [95% CI 0.45, 0.84]), as was the error rate with a potential clinical significance. The contribution of handwritten medication orders to the total amount of medication orders was significantly decreased after the intervention (12.8% vs 20.6% [95% CI 4.6, 11.0]) and the administration of a drug ordered by a handwritten medication order resulted in a significantly higher administration rate than with administration of a drug ordered by a printed medication order (before the intervention 20.7% vs 8.0%, OR 2.99 [95% CI 1.96, 4.56], after the intervention 11.4% vs 5.6%, OR 2.18 [95% CI 1.16, 4.11]). CONCLUSION: This observational study shows a significant reduction in clinically relevant, administration and (therapeutic) prescription error rates when applying a system using computerised and daily updated medication charts compared with a system using traditional medication charts. Therefore, the use of computerised and daily updated medication charts has the potential to improve the quality of the medication distribution process in hospitals waiting for the implementation of a computerised physician order entry system.

Aged↗
PubMed1994

Quality of antidepressant medications prescribed at discharge to depressed elderly patients in general medical hospitals before and after prospective payment system.

This study describes the quality of antidepressant medication use at hospital discharge for depressed elderly inpatients and compares quality of care before and after implementation of Medicare's Prospective Payment System (PPS). The study reviewed data from medical records of 2746 depressed, elderly, hospitalized patients in acute-care general medical hospitals in five U.S. states (pre-PPS period 1981-82; post-PPS period 1985-86). The majority were discharged on antidepressant medication both pre-PPS and post-PPS. After PPS' implementation, sedating medications were used less often in all treatment settings. In general medical wards, a higher percentage post-PPS (24%) than pre-PPS (14%) were discharged 48 hours or less after first starting an antidepressant medication. In both time periods, one-third of patients receiving antidepressant medications were prescribed daily dosages at discharge below recommended, minimum, therapeutic levels, whether treated in general medical wards or psychiatric units. Otherwise, patients previously treated in psychiatric units received higher quality of medication management than those treated in general medical wards. Over time, patients discharged on antidepressant medication were less likely to use sedating medication, suggesting improved quality of care. In general medical wards, however, patients were discharged more rapidly after starting medication, possibly suggesting lower quality of care. A substantial percentage of patients received subtherapeutic dosages of medication or sedating medications, suggesting that improved management of discharge antidepressant medication in the elderly is needed in general medical hospitals.

Adaptation, Psychological↗
PubMed2006

Relationships between beliefs about medications and nonadherence to prescribed chronic medications.

BACKGROUND: Medication beliefs of patients with a specific medical condition have been associated with nonadherence to drugs used to treat that condition. However, associations between medication beliefs and nonadherence of individuals on chronic, multiple medications have not been studied. OBJECTIVE: To investigate associations between patients' medication beliefs and nonadherence to chronic drug therapy. METHODS: A cross-sectional, self-administered survey of patients waiting to see pharmacists at an outpatient pharmacy in a primary care clinic was conducted. Participants' medication beliefs were assessed using the Beliefs about Medicines Questionnaire, and nonadherence was assessed using the Morisky Medication Adherence Scale. Pearson correlation analysis was used to assess bivariate associations between medication beliefs and nonadherence. Regression was used to assess relative strength of associations between various medication beliefs and nonadherence and also to assess the significance of the interactions between those beliefs and nonadherence. RESULTS: There were positive bivariate associations between specific concerns about medications (p < 0.001), perceived general harmful effects of medications (p < 0.001), and perceived overprescribing of medications by physicians (p < 0.001) and medication nonadherence. When relative strength of associations between each medication belief and nonadherence was assessed, while controlling for other medication beliefs, specific-necessity (p = 0.02) and specific-concerns (p = 0.01) exhibited significant negative and positive associations with nonadherence, respectively. All two-way interactions between variables in the model were insignificant. A model consisting of age, total number of drugs used, and medication beliefs, that is, specific-necessity, specific-concerns, general-overuse, and general-harm, accounted for 26.5% of variance. Medication beliefs alone explained 22.4% of variation in nonadherence to chronic drug therapy. CONCLUSIONS: Patients' medication beliefs explained a significant portion of variation in medication nonadherence.

Adult↗
PubMed2000

[Does disclosure of medical records to patients influence medical care?].

UNLABELLED: Disclosure of medical records to patients has been assessed, but the influence of disclosure on medical care has not been well researched. To address this situation, this study was conducted to test three hypotheses: 1) doctors think that the disclosure can influence medical care, 2) whether doctors think disclosure can influence medical care depends on how they rank medical records (for example, as evidence for diagnosis and treatment, a process to reach a diagnosis, a tool to communicate with other medical staff, etc), and on whether they think medical record disclosure could change the content of the record. Questionnaires were sent to 881 doctors who work at two hospitals affiliated with N Medical School and K Medical University. RESULTS: Four hundred eighty-eight doctors responded. The findings related to the hypotheses were as follows: 1) Those who answered that the disclosure could have an influence on medical care were 49.1% of the total. 2) There was no relation between how doctors ranked medical records and whether they thought disclosure could influence medical care. 3) Doctors who answered that there were things that they would not be able to write if medical records were disclosed accounted for 73.5% of the total. These doctors answered "yes" significantly higher to the question that the disclosure could influence medical care than others (Odds 3.6, P < 0.01). Doctors who thought they would not be able to enter the diagnosis, the name of the disease for insurance, self-evaluation, information that could be judged and subjective information answered that disclosure could influence on medical care (P < 0.05). It was assumed that disclosure of medical records to patients could change the content of the medical records and that could influence medical care. How to deal with information will become an important issue if records are disclosed.

Attitude of Health Personnel↗
PubMed2002

Measuring medication: do interviews agree with medical record and pharmacy data?

BACKGROUND: Medication measurement is crucial in assessing quality for chronic conditions yet agreement of alternate data sources remains uncertain. OBJECTIVES: To evaluate medication agreement between interviews, medical records, and pharmacy data; to assess data source contribution to attributing medication exposure; and to describe the impact of combining data sources on models that predict medication use. RESEARCH DESIGN: Prospective cohort study. SUBJECTS: Probability sample of HIV-infected participants in the HIV Cost and Services Utilization Study. MEASURES: Medications reported in 2267 interviews, 1936 medical records, and 457 pharmacy records were compared using crude agreement, kappa, and the proportion of average positive and negative agreement. The percent of medications reported in each source alone was used to assess their relative contribution to attributing exposure status. We performed weighted logistic regression in alternate data sources. RESULTS: Kappa varied from 0.38 for nucleoside reverse transcriptase inhibitors to 0.70 for protease inhibitors, when comparing drug classes in interview versus medical record, interview versus pharmacy data, and medical record versus pharmacy data. The percentage of medications reported in medical records was greater than that reported in interviews or pharmacy data. Pharmacy data contributed little to the attribution of medication exposure. Adding medication data to interview data did not appreciably change analytic models predicting medication use. CONCLUSIONS: For specific medications, agreement between alternative data sources is fair to substantial, but is lower for key drug classes. Relying on one data source may lead to misclassification of drug exposure status, but combining data sources does not change the results of analytic models predicting appropriate medication use.

Antiretroviral Therapy, Highly Active↗