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Record W2340494696 · doi:10.1111/acem.12985

Hot Off the Press: Use of Shared Decision‐making for Management of Acute Musculoskeletal Pain in Older Adults Discharged From the Emergency Department

2016· letter· en· W2340494696 on OpenAlexaff
Kevin Cullison, Christopher R. Carpenter, William K. Milne

Bibliographic record

VenueAcademic Emergency Medicine · 2016
Typeletter
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsWestern University
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineEmergency departmentEthnic groupPain managementEmergency medicinePhysical therapyPsychiatry

Abstract

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The proportion of patients over age 65 who seek care in the emergency department (ED) has been increasing for decades and many of these visits are related to musculoskeletal injury or pain.1, 2 The elderly are less likely to have their pain appropriately assessed and managed in the ED in comparison to younger adults, although sex and ethnic disparities in oligoanalgesia occur across all age groups.3, 4 Emergency medicine resident geriatric core competencies include the assessment and management of pain in accordance with the patient's goals of care.5 In spite of this, the Society for Academic Emergency Medicine Geriatric Task Force identified pain management in the elderly as one condition for which there is a significant quality gap.6 Pain management in aging adults is a complex challenge because physicians must weigh the risks of adverse events due to drug–drug interactions and drug–disease interactions against the harms of ineffective analgesia, which include prolonged functional recovery, length of stay, and pain duration, as well as increased risk of falls or frailty progression.7, 8 Shared decision-making (SDM) may be a viable solution to this challenge as it allows patients to make informed decisions among several reasonable treatment options in a way that helps preserve autonomy and respect individual values.9, 10 This was a prospective study on a convenience sampling of patients over age 60 years old who presented to a single academic ED between September 2012 and April 2015. Patients were interviewed if they had musculoskeletal pain (determined via chart review by the principal investigator) of at least moderate intensity and of less than 1-month duration. Patient's desired degree of inclusion in medical decision-making was quantified using the Control Preferences Scale,11 while the 9-item Shared Decision-Making Questionnaire (SDM-Q-9) assessed individual's perceptions of the degree to which analgesic-choice SDM actually occurred in the ED.12 Patients were ultimately discharged with a prescription for opioids, acetaminophen, or nonsteroidal anti-inflammatory drugs (NSAIDs) and had two additional postdischarge telephone follow-ups: 1) within 1 day to assess their satisfaction with the analgesic selection and their overall impression of whether or not SDM occurred during their ED visit and 2) at 6–10 days to assess their pain symptoms and medication side effects. The primary outcome was the change in pain severity from the time of ED arrival to the 1-week follow-up. Secondary outcomes included patient satisfaction with analgesia decision at time of discharge and patient analgesia satisfaction. There were several limitations to this study. Because this was an observational study rather than a randomized controlled study, the positive impact of SDM on patient satisfaction may be overestimated. Since only a convenience sampling of patients was enrolled, a selection bias favoring those individuals who are more engaged in healthcare decision-making may exist. Although the authors appropriately performed their power analyses a priori and met their enrollment goals, 1-week follow-up was obtained for only 59.9% of patients (94 of 157) introducing possible attrition bias. This was not an interventional study so the actual delivery of SDM was not controlled or evaluated. Instead, this observational study sought to understand how willing older adults would be to engage in SDM and then their perceptions of whether SDM occurred postvisit. The study does not evaluate whether or how the providers actually communicated the comparative effectiveness of different analgesic treatment options to the patients or if patients understood these facets of pain medication options. Finally, the authors used instruments, such as the SDM-Q-9 questionnaire, to evaluate the patient–provider SDM interaction, although this tool has previously been used in primary care setting and it is unclear whether it accurately captures SDM occurrences in ED settings.12 As the authors point out, an alternative approach to evaluating SDM is the "Observing Patient Involvement in Decision-Making" (OPTION) scale, which helps eliminate recall bias through the use of third-party video review.13 The authors observed a mean reduction in pain score of 2.1 points (0–10 scale) between the initial ED visit and 1-week follow-up, although this reduction was not associated with patient perception of SDM as estimated by the SDM-Q-9 tool. SDM was, however, associated with greater patient satisfaction with the chosen analgesic (p = 0.002). There was no association between the degree of SDM and the percentage of patients receiving opioids (p = 0.06), acetaminophen (p = 0.4), or NSAIDs (p = 0.1). In regard to patient preferences in using SDM for analgesic selection, 16% were categorized as "active" for wanting to make their own informed decision, 47% as "passive" for wanting the physician to make the final treatment decision, and the remaining 37% as "collaborative" for preferring a SDM approach with the provider. Patients preferring a more active role in analgesic selection were more likely to be college educated, receive care from a nurse practitioner (as opposed to a physician), and receive care from a female (vs. male) provider. SDM involves an exchange of information between a provider and a patient, followed by a healthcare-related decision based on varying degrees of collaboration between these parties. When considering use of SDM in everyday management decisions involving the elderly population, it is important to assess for and accommodate underlying deficiencies in cognition and health literacy because these issues may impede effective and ethical SDM. Although this study was limited by its observational design, the results suggest that SDM may hold value for managing acute musculoskeletal pain in aging populations. This study also demonstrated that provider-specific characteristics may influence patients' desire to participate in the decision-making process. In the future, the true impact of SDM on optimal analgesic selection in the ED will need to be assessed with a randomized, controlled trial involving a systematic SDM intervention. Source: The Commonwealth Fund (https://medium.com/@CommonwealthFund/is-it-time-to-bring-consumer-data-into-health-care-f4cdf1fd2588#.jyj36x2eg) SDM was associated with increased patient satisfaction for analgesic selection, but not with a change in pain severity or with the type of analgesic prescribed.

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)0.005
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.158
GPT teacher head0.443
Teacher spread0.285 · 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 designObservational
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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Citations4
Published2016
Admission routes1
Has abstractyes

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