Improving patient satisfaction: Timely feedback to specific physicians is essential for success
Bibliographic record
Abstract
Patient satisfaction has received increased attention in recent years, which we believe is well deserved and long overdue. Anyone who has been hospitalized, or has had a loved one hospitalized, can appreciate that there is room to improve the patient experience. Dedicating time and effort to improving the patient experience is consistent with our professional commitment to comfort, empathize, and partner with our patients. Though patient satisfaction itself is an outcome worthy of our attention, it is also positively associated with measures related to patient safety and clinical effectiveness. Moreover, patient satisfaction is the only publicly reported measure that represents the patient’s voice, and accounts for a substantial portion of the Centers for Medicare and Medicaid Services payment adjustments under the Hospital Value Based Purchasing Program. However, all healthcare professionals should understand some key fundamental issues related to the measurement of patient satisfaction. The survey from which data are publicly reported and used for hospital payment adjustment is the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, developed by the Agency for Healthcare Research and Quality. HCAHPS is sent to a random sample of 40% of hospitalized patients between 48 hours and 6 weeks after discharge. The HCAHPS survey uses ordinal response scales (eg, never, sometimes, usually, always) that generate highly skewed results toward favorable responses. Therefore, results are reported as the percent “top box” (ie, the percentage of responses in the most favorable category) rather than as a median score. The skewed distribution of results indicates that most patients are generally satisfied with care (ie, most respondents do not have an axe to grind), but also makes meaningful improvement difficult to achieve. Prior to public reporting and determination of effect on hospital payment, results are adjusted for mode of survey administration and patient mix. The same is not true when patient satisfaction data are used for internal purposes. Hospital leaders typically do not perform statistical adjustment and therefore need to be careful not to make “apples-to-oranges”–type comparisons. For example, obstetric patient satisfaction scores should not be compared to general medical patient satisfaction scores, as these populations tend to rate satisfaction differently. The HCAHPS survey questions are organized into domains of care, including satisfaction with nurses and satisfaction with doctors. Importantly, other healthcare team members may influence patients’ perception in these domains. For example, a patient responding to nurse communication questions may also reflect on experiences with patient care technicians, social workers, and therapists. A patient responding to physician communication questions might also reflect on experiences with advanced practice providers. A common mistake is the practice of attributing satisfaction with doctors to the individual who served as the discharge physician. Many readers have likely seen patient satisfaction reports broken out by discharge physician with the expectation that giving this information to individual physicians will serve as useful formative feedback. The reality is that patients see many doctors during a hospitalization. To illustrate this point, we analyzed data from 420 patients admitted to our nonteaching hospitalist service who had completed an HCAHPS survey in 2014. We found that the discharge hospitalist accounted for only 34% of all physician encounters. Furthermore, research has shown that patients’ experiences with specialist physicians also have a strong influence on their overall satisfaction with physicians. Having reliable patient satisfaction data on specific individuals would be a truly powerful formative assessment tool. In this issue of the Journal of Hospital Medicine, Banka and colleagues report on an impressive approach incorporating such a tool to give constructive feedback to physicians. Since 2006, the study site had administered surveys to hospitalized patients that assess their satisfaction with specific resident physicians. However, residency programs only reviewed the survey results with resident physicians about twice a year. The multifaceted intervention developed by Banka and colleagues included directly emailing the survey results to internal medicine resident physicians in real time while they were in service, a 1-hour conference on best communication practices, and a reward program in which 3 residents were *Address for correspondence and reprint requests: Kevin J. O’Leary, MD, Associate Professor of Medicine, Division of Hospital Medicine, Northwestern University Feinberg School of Medicine, 211 E. Ontario St., Chicago, IL 60611; Telephone: 312-926-5984; Fax: 312-926-4588; E-mail: keoleary@nmh.org
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".