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Record W80640670 · doi:10.1093/pch/14.2.76

Medication reconciliation: Coming to a hospital near you

2009· article· en· W80640670 on OpenAlexaff
Maitreya Coffey

Bibliographic record

VenuePaediatrics & Child Health · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

A seven-year-old child with multiple medical problems, including seizures, was admitted with aspiration pneumonia. He was on six medications at home. Instead of his regular medication (clobazam), clonazepam was ordered. Three days later, he developed decreased consciousness, and was transferred to the intensive care unit and intubated. After multiple failed attempts at extubation, the pharmacist discovered that the admission medication error resulted in an overdose, causing the neurological deterioration and prolonged intensive care unit stay. Up to one-quarter of health care-related adverse events involve medications (1,2). Medication errors may happen at several stages, including prescription, preparation and administration. Recently, the process of admission to hospital has emerged as a key area of focus. Up to 50% of patients have at least one error in their hospital admission medication history (3). This may not surprise the practicing clinician who is familiar with time constraints, limited health literacy of patients and a lack of accessible, integrated health records. What may be surprising is that a substantial portion of discrepancies between home and inpatient medications are clinically relevant and have the potential to cause adverse events (4). In response to this problem, the concept of medication reconciliation has emerged. The term medication reconciliation (or ‘Med Rec’), attributed to Jane Justeson, means “a formal process of obtaining a complete and accurate list of each patient's current home medications – including name, dosage, frequency and route – and comparing the physician's admission, transfer, and discharge orders to that list. Discrepancies are brought to the attention of the prescriber and, if appropriate, changes are made to the orders” (5). Because this process has dramatically reduced discrepancy rates in various settings (6–8), it has been endorsed by leading international patient safety organizations (9,10). The Canadian Patient Safety Institute (a nonprofit organization established with funding from Health Canada in 2003), whose mandate is to provide coordination and leadership in advancing patient safety, included Med Rec in its Safer Healthcare Now! campaign to implement six specific safety strategies nationally (11). Now that it has been designated to be a required organizational practice by the hospital accreditation authorities in Canada and the United States, it will eventually impact all of us who care for hospitalized patients (12,13). In 2005, our paediatric medicine unit at The Hospital for Sick Children (Toronto, Ontario), along with 18 other academic and community paediatric centres across Canada, embarked on the implementation of Med Rec in a collaboration organized by the Canadian Association of Paediatric Health Centres. To better understand the degree of the problem in our unit, we audited a random sample of charts using the methods outlined in the Safer Healthcare Now! Getting Started Kit (11). We found that each patient had numerous different medication histories documented by various trainees, nurses and physicians. However, fewer than 10% of the charts had a complete list of medications. Of the medications documented, only one-half were included in the admission medication orders, but there was no indication of the physician's intent to discontinue or change medications. To begin to evaluate the potential impact of Med Rec on our patients, we collected data prospectively on a convenience sample of patients. For these patients, a pharmacist performed a ‘best possible medication history’ (BPMH). This is a structured interview of the patient and family, pursuing all sources of information necessary, such as drug vials, referring physician records and community pharmacies (6). Our pharmacists typically used two to four sources and spent 15 min to 30 min, reflecting a more thorough pursuit of information compared with a standard physician history. Of our sample, 40% of the patients had at least one unintentional discrepancy (ie, error) found by the pharmacist, similar to findings reported in the adult literature (4). Appropriately, physicians in our group questioned the clinical relevance of the discrepancies detected. An adult study (5) had shown that approximately 40% of these discrepancies are potentially serious, but no data in children were available. Among the discrepancies our small pilot study detected (n=20), one was judged as potentially serious in the immediate period, and several others could have become serious if missed for a prolonged admission or inadvertently discontinued at discharge. Examples of the unintentionally omitted medications included inhaled corticosteroids, calcium and vitamin D supplements, and medications for constipation and gastroesophageal reflux disease. Reasons cited for the discrepancies included, ‘I didn't know the patient was on that’, ‘I forgot to order it’ and ‘mom's and dad's medication histories were different’. To remedy these problems, we designed a Med Rec process in which our physicians are expected to document their standard medication history on a specially designed form, which includes their intention to continue, discontinue or change each medication recorded. Later, another team member (nurse or pharmacist) builds on this by performing a formal BPMH. This is then compared with the admission medication orders, and if discrepancies are identified, they are discussed with the physician and corrected as necessary. While there is strong evidence that pharmacist-obtained histories are the gold standard (3), it was not deemed feasible or necessary to obtain them for every patient. Thus, nurses were trained to perform a BPMH, reconcile it with the admission orders and to only contact a pharmacist for patients with complex or high-risk home regimens. In the year following implementation of Med Rec, physician compliance was inconsistent, ranging from 30% to 80%. Convincing colleagues to adopt Med Rec proved more challenging than anticipated. One reason may be that it has not been reviewed in detail in the paediatric literature. Publications are found in the pharmacy, quality improvement and general medical literature, and they focus on adult patients (3–8). After extensive education efforts, physicians reported being convinced of the value but often forgot to do it. The constant turnover of trainees and faculty frequently overwhelmed the implementation team's ability to educate and remind each new provider. Dedicated human resources for the project were necessary to employ implementation strategies, such as frequent auditing and feedback. Recently, we began to send regular feedback of individual physicians' compliance rates to the physician group and our Vice President for Quality and Academic Affairs. Since then, physician compliance has been consistently near 90%. This represents a substantial success, but the audit and feedback technique is time-consuming and cannot be provided indefinitely. Centres implementing Med Rec in Canada and around the world have reported similar challenges (personal communication). Given the lack of paediatric Med Rec evidence, we have since conducted a rigorous study (14) showing that potentially serious admission medication errors occur in children at rates similar to those in adults. Interestingly, in our population, antiepileptic drugs were most strongly associated with clinically significant errors. Although these data were informative, we observed that physicians responded more strongly to anecdotes from our own unit, such as the case above. In fact, we experienced a subsequent case in which the same clobazam/clonazepam error was made, but it was caught by Med Rec before any harm resulted. This illustrates the value of Med Rec more powerfully than our statistics. When planning a Med Rec implementation, consider it a principle rather than a specific process. Who performs the steps and in what order may be tailored for each setting. Involve clinical pharmacists with high-risk patients wherever possible; they provide the Med Rec gold standard but should not be held exclusively responsible for medication safety. Successful implementations have been led by pharmacists, nurses or physicians, but project management resources may be necessary. Use local anecdotes of medication errors to illustrate the value of Med Rec, but remember that buy-in will not necessarily result in compliance. Consider sustainability carefully in designing your process, especially in a setting of frequent staff or trainee turnover. Audit and feedback is helpful for implementation, but it cannot be relied on indefinitely. The key to a sustainable Med Rec initiative is to build it into existing workflow and make it the default process (eg, embed it into a preprinted history and physical form). Med Rec should facilitate seamless flow of information across the admission-transfer-discharge continuum and create opportunities for physicians to save time at each step. A proposed solution is an electronic admission medication list, which may be modified and converted into admission orders, transfer summaries and discharge prescriptions. Although vendors have been slow to produce high-quality applications for Med Rec, newer versions show promise. Finally, be prepared for this seemingly simple concept to be much more complex in the real world and to prove, as always, that ‘the devil is in the details’. The author thanks Kim Streitenberger, Lynn Mack and Margot Follett-Rowe for their tenacious work implementing Med Rec, and Drs Ron Laxer and Jeremy Friedman for their support of the initiative.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.374
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2009
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