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Patient Satisfaction Surveys and the Emergency Department

2004· article· en· W2324932930 on OpenAlexaboutno aff
Steven J. Davidson

Bibliographic record

VenueEmergency Medicine News · 2004
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentPatient satisfactionMedicinePediatric emergency medicineFamily medicineRealmQuarter (Canadian coin)Medical emergencyPsychologyNursingEmergency physician

Abstract

fetched live from OpenAlex

FigureWe just received preliminary (and sobering) results of our fourth-quarter patient satisfaction survey. The process we use differs from the usual commercial patient satisfaction survey, but its value to the ED is so great that I thought it might be worth sharing with you. I've also recently learned of another approach — driven by the emergency physician group practice — that may hold some interest for you. While neither may work for your environment, perhaps you can learn something you can use. Our hospital conducts structured telephone interviews on admitted and discharged patients, talking to sufficient numbers of patients to garner completed interviews with 100 patients each month. Patients are interviewed in their language of choice, either English, Spanish, Yiddish, Russian, or more recently Mandarin Chinese. Arabic may be next. Patient satisfaction surveys remind ED staff that caring for the patient must go beyond the clinical realm alone During October, November, and December, pediatric inpatients and ambulatory patients are surveyed. We also survey ED outpatients and the caregivers of pediatric ED outpatients using an instrument similar to our adult/pediatric inpatient tool. The inpatient tool attempts to determine by direct questioning and confirmatory questions whether the patient was admitted through the ED. If he was, seven questions relevant to the ED experience are asked, only one of which is about the emergency physician. The question is broad, and asks about all physicians that may have been involved in the patient's care. We do not tie patients' comments to specific providers. The emergency department and the hospital administration look at the survey results as useful information about systems and processes, not individual providers. What we do get is wonderful information that allows us to compare the experience of admitted patients with discharged patients as well as longitudinal results over time. Results include an analysis of both positives and negatives. We aim for no more than 10 percent in the “strongly disagree” category and 90 percent in the “agree” and “strongly agree” groups. Survey items are all posed in the affirmative. Our results since 2001 (the survey is conducted monthly; results are reported quarterly) conform to Boudreaux and O'Hea's findings that patient satisfaction is most affected by the quality of interaction with the ED provider (J Emerg Med 2004;26[1]:13). Waiting time, perceived or real, and discordance among perception and expectation were a secondary factor in this literature review and analysis of opportunities for further study. Variations on a Theme I've learned of two variations on an emergency physician-centered patient satisfaction survey, both of which I would characterize more as callback efforts intent on enriching the patient encounter and improving patient satisfaction. In both instances, the emergency physician practices conduct the process. Tom Scaletta, MD, the chairman of the department of emergency medicine at Edward Hospital in Naperville, IL, designed and implemented the process, and he describes his approach like this: “The callback clerk attempted to reach a cohort of all discharged patients. She called about 3,000 a month, and reached about one-third. What she said was carefully scripted. Patients getting worse were told to call their [primary care provider] or come back to the ED immediately. No further medical advice was given by the callback clerk, though, if requested, the call was transferred to a nurse. The cost (about $36K a year) was about $1 per patient attempted or $3 per patient reached. This was a full-time position by an administrative assistant with great interpersonal skills. Her job was facilitated by a callback database (FileMaker Pro), which uploaded the prior day's census, automatically dialed, and served as a user-friendly way to store the data.” (J Emerg Med 2004;26[1]:13.) The callback information was used to improve satisfaction — the act of checking on patient's well being was positively received. We uncovered problems quickly, and made recommendations to correct them, documented improvement, and ensured adequate follow-up. All this helps minimize risk. Finally, we collected data on opportunities for the physician, nurse, and system to improve. We used a letter grade because patients immediately understood what we meant. A portion of the doctors' bonus was tied to this grade. Overall, 70 percent of the doctors received an A, and 25 percent received a B. The ratio of A:B was more influential than A/B:C/D/F in comparing the physicians. They received monthly feedback, and many were able to change their means of interacting with patients to improve their scores significantly. The other physician-driven approach takes advantage of integrated information systems and telecommunications technologies to use automated dialing to leave a message in the physician's voice to the patient. The message advises the patient to call 9–1-1, return to the ED, or see his primary care provider if he is not improved or has gotten worse. It then solicits feedback with any concerns or comments regarding the patient's care or experience in the ED. A patient request for a callback from the physician receives that response. Our patient satisfaction survey results served as a sharp rejoinder to complacency, reminding me that while excuses abound — space, staffing resources, upstairs-downstairs communication, and conflicts — I must periodically remind myself and re-energize all staff in our commitment to care for our patients within the resources available. Patients and their families take for granted that they are getting good clinical care; caring for the patient must go beyond the clinical realm alone.

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)0.012
metaresearch head score (Gemma)0.055
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.023
GPT teacher head0.304
Teacher spread0.280 · 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
GenreEmpirical

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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Citations0
Published2004
Admission routes1
Has abstractyes

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