Assessing the relationship between patient satisfaction and clinical quality in an ambulatory setting

Examining how physician factors influence patient satisfaction during clinical consultations about cancer prognosis and pain

Objective

Patient-physician communication affects cancer patients' satisfaction, health outcomes, and reimbursement for physician services. Our objective is to use machine learning to comprehensively examine the association between patient satisfaction and physician factors in clinical consultations about cancer prognosis and pain.

Methods

We used data from audio-recorded, transcribed communications between physicians and standardized patients (SPs). We analyzed the data using logistic regression (LR) and random forests (RF).

Results

The LR models suggested that lower patient satisfaction was associated with more in-depth prognosis discussion; and higher patient satisfaction was associated with a greater extent of shared decision making, patient being black, and doctor being young. Conversely, the RF models suggested the opposite association with the same set of variables.

Conclusion

Somewhat contradicting results from distinct machine learning models suggested possible confounding factors (hidden variables) in prognosis discussion, shared decision-making, and doctor age, on the modeling of patient satisfaction. Practitioners should not make inferences with one single data-modeling method and enlarge the study cohort to help deal with population heterogeneity.

Innovation

Comparing diverse machine learning models (both parametric and non-parametric types) and carefully applying variable selection methods prior to regression modeling, can enrich the examination of physician factors in characterizing patient-physician communication outcomes.

Composite measures of quality of health care: Evidence mapping of methodology and reporting

Background

Quality indicators are used to quantify the quality of care. A large number of quality indicators makes assessment of overall quality difficult, time consuming and impractical. There is consequently an increasing interest for composite measures based on a combination of multiple indicators.

Objective

To examine the use of different approaches to construct composite measures of quality of care and to assess the use of methodological considerations and justifications.

Methods

We conducted a literature search on PubMed and EMBASE databases (latest update 1 December 2020). For each publication, we extracted information on the weighting and aggregation methodology that had been used to construct composite indicator(s).

Results

A total of 2711 publications were identified of which 145 were included after a screening process. Opportunity scoring with equal weights was the most used approach (86/145, 59%) followed by all-or-none scoring (48/145, 33%). Other approaches regarding aggregation or weighting of individual indicators were used in 32 publications (22%). The rationale for selecting a specific type of composite measure was reported in 36 publications (25%), whereas 22 papers (15%) addressed limitations regarding the composite measure.

Conclusion

Opportunity scoring and all-or-none scoring are the most frequently used approaches when constructing composite measures of quality of care. The attention towards the rationale and limitations of the composite measures appears low.

Discussion

Considering the widespread use and the potential implications for decision-making of composite measures, a high level of transparency regarding the construction process of the composite and the functionality of the measures is crucial.

The Impact of Diagnostic Decisions on Patient Experience in the Pediatric Emergency Department

OBJECTIVE

Patient experience serves as both a subjective measure of value-based health care delivery and a metric to inform operational decision making. The objective of this study was to determine if specific diagnostic and therapeutic interventions affect patient experience scores for children seen in the emergency department.

METHODS

We performed a retrospective observational study in the emergency department of a large quaternary care children's hospital on patients who were discharged to home and later completed a National Research Corporation Health patient experience survey. We matched the survey results to electronic health record (EHR) data and were able to extract demographics, operational metrics, and order information for each patient. We performed multiple logistic regression analyses to determine the association of image acquisition, laboratory test ordering, medication administration, and discharge prescribing with likelihood to recommend the facility as our measure of patient experience.

RESULTS

Of the 4103 patients who met inclusion criteria for the study, 75% strongly recommended the facility. Longer wait times were associated with lower patient experience scores [odds ratio (OR) per waiting room hour increase, 0.72; 95% confidence interval (CI), 0.65-0.81]. Significant diagnostic factors associated with higher patient experience included magnetic resonance imaging ordering (OR, 2.38; 95% CI, 1.00-5.67), x-ray ordering (OR, 1.19; 95% CI, 1.00-1.42), and electrocardiogram ordering (OR, 1.62; 95% CI, 1.07-2.44). Of the treatment factors studied, only antibiotic prescribing at discharge was found to have a significant positive association with patient experience (OR, 1.32; 95% CI, 1.08-1.63).

CONCLUSION

The positive association between more intensive diagnostic workups and patient experience could have implications on the utility of patient experience scores to evaluate pediatric care teams. Consideration should be taken to interpret patient experience scores in the context of compliance with approaches in evidence-based medicine.