Specialties differ in which aspects of doctor communication predict overall physician ratings. Quigley Denise D,Elliott Marc N,Farley Donna O,Burkhart Q,Skootsky Samuel A,Hays Ron D Journal of general internal medicine BACKGROUND:Effective doctor communication is critical to positive doctor-patient relationships and predicts better health outcomes. Doctor communication is the strongest predictor of patient ratings of doctors, but the most important aspects of communication may vary by specialty. OBJECTIVE:To determine the importance of five aspects of doctor communication to overall physician ratings by specialty. DESIGN:For each of 28 specialties, we calculated partial correlations of five communication items with a 0-10 overall physician rating, controlling for patient demographics. PATIENTS:Consumer Assessment of Healthcare Providers and Systems Clinician and Group (CG-CAHPS®) 12-month Survey data collected 2005-2009 from 58,251 adults at a 534-physician medical group. MAIN MEASURES:CG-CAHPS includes a 0 ("Worst physician possible") to 10 ("Best physician possible") overall physician rating. Five doctor communication items assess how often the physician: explains things; listens carefully; gives easy-to-understand instructions; shows respect; and spends enough time. KEY RESULTS:Physician showing respect was the most important aspect of communication for 23/28 specialties, with a mean partial correlation (0.27, ranging from 0.07 to 0.44 across specialties) that accounted for more than four times as much variance in the overall physician rating as any other communication item. Three of five communication items varied significantly across specialties in their associations with the overall rating (p < 0.05). CONCLUSIONS:All patients valued respectful treatment; the importance of other aspects of communication varied significantly by specialty. Quality improvement efforts by all specialties should emphasize physicians showing respect to patients, and each specialty should also target other aspects of communication that matter most to their patients. The results have implications for improving provider quality improvement and incentive programs and the reporting of CAHPS data to patients. Specialists make important contributions to coordinated patient care, and thus customized approaches to measurement, reporting, and quality improvement efforts are important. 10.1007/s11606-013-2663-2

[Doctor-Patient relationship in healthcare institutions of Medellin, Colombia]. Revista de salud publica (Bogota, Colombia) OBJECTIVE:To understand the perceptions that doctors and patients have about their relationships and how the current conditions of the General System of Social Security in Health (GSSSH) influence their relationship. MATERIALS AND METHODS:The collection and analysis of information was based on the saturation principle proposed by qualitative research, through direct observation and semi-structured interviews applied to 17 patients and 15 physicians during the exercise of their roles, in the waiting and consultation environment within the selected healthcare institutions. The interviews recorded, transcribed and analyzed under five precepts of the Grounded Theory of Corbin and Strauss. The sample design was theoretical for convenience. RESULTS:Health system conditions on the doctor's doing, abuse of the right by some patients, the perception of uneven quality between health care promotion entities, communication failures in the Doctor-Patient Relationships, loss of prestige of the general doctors, perception of disbelief towards young doctors, among other identified perceptions, hint that changes in the GSSSH contribute to the construction of current Doctor-Patient Relationships. CONCLUSIONS:It is essential that the actors of the GSSSH propose interventions that reinforce the communicative and psychosocial capacities in the doctors from their formative processes, as well as the provision of the GSSSH to provide conditions that allow the doctor to develop a health care focused on the patient. 10.15446/rsap.V21n4.80095

Improving patient self-description in Chinese online consultation using contextual prompts. BMC medical informatics and decision making BACKGROUND:Online health care consultation has been widely adopted to supplement traditional face-to-face patient-doctor interactions. Patients benefit from this new modality of consultation because it allows for time flexibility by eliminating the distance barrier. However, unlike the traditional face-to-face approach, the success of online consultation heavily relies on the accuracy of patient-reported conditions and symptoms. The asynchronous interaction pattern further requires clear and effective patient self-description to avoid lengthy conversation, facilitating timely support for patients. METHOD:Inspired by the observation that doctors talk to patients with the goal of eliciting information to reduce uncertainty about patients' conditions, we proposed and evaluated a machine learning-based computational model towards this goal. Key components of the model include (1) how a doctor diagnoses (predicts) a disease given natural language description of a patient's conditions, (2) how to measure if the patient's description is incomplete or more information is needed from the patient; and (3) given the patient's current description, what further information is needed to help a doctor reach a diagnosis decision. This model makes it possible for an online consultation system to immediately prompt a patient to provide more information if it senses that the current description is insufficient. RESULTS:We evaluated the proposed method by using classification-based metrics (accuracy, macro-averaged F-score, area under the receiver operating characteristics curve, and Matthews correlation coefficient) and an uncertainty-based metric (entropy) on three Chinese online consultation corpora. When there was one consultation round, our method delivered better disease prediction performance than the baseline method (No Prompts) and two heuristic methods (Uncertainty-based Prompts and Certainty-based Prompts). CONCLUSION:The disease prediction performance correlated with uncertainty of patients' self-described symptoms and conditions. However, heuristic solutions ignored the context to decrease large amounts of uncertainty, which did not improve the prediction performance. By elaborate design, a machine-learning algorithm can learn the inner connection between a patient's self-description and the specific information doctors need from doctor-patient conversations to provide prompts, which can enrich the information in patient self-description for a better performance in disease prediction, thereby achieving online consultation with fewer rounds of doctor-patient conversation. 10.1186/s12911-022-01909-3

Doctor-patient communication: a review and a rationale for using an assessment framework. Belasen Ariel,Belasen Alan T Journal of health organization and management PURPOSE:The purpose of this paper is to explore the extent to which improving doctor-patient communication (DPC) can address and alleviate many healthcare delivery inefficiencies. DESIGN/METHODOLOGY/APPROACH:The authors survey causes and costs of miscommunication including perceptual gaps between how physicians believe they perform their communicative duties vs how patients feel and highlight thresholds such as the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) used by hospitals to identify health outcomes and improve DPC. FINDINGS:The authors find that DPC correlates with better and more accurate care as well as with more satisfied patients. The authors utilize an assessment framework, doctor-patient communication assessment (DPCA), empirically measuring the effectiveness of DPC. While patient care is sometimes viewed as purely technical, there is evidence that DPC strongly predicts clinical outcomes as well as patients' overall ratings of hospitals. RESEARCH LIMITATIONS/IMPLICATIONS:More research is needed to extend our understanding of the impact of the DPC on the overall HCAHPS ratings of hospitals. The authors think that researchers should adopt a qualitative method (e.g. content analysis) for analyzing DPC discourse. PRACTICAL IMPLICATIONS:When a sufficient amount of DPCA training is initiated, a norming procedure could be developed and a database may be employed to demonstrate training program's efficacy, a critical factor in establishing the credibility of the measurement program and nurturing support for its use. ORIGINALITY/VALUE:The authors highlight clinical and operational issues as well as costs associated with miscommunication and the need to use metrics such as HCAHPS that allow consumers to see how hospitals differ on specific characteristics. 10.1108/JHOM-10-2017-0262

Verbal communication skills and patient satisfaction. A study of doctor-patient interviews. Rowland-Morin P A,Carroll J G Evaluation & the health professions This research attempted to quantify specific behaviors in the physician's initial interviewing style and relate them to patients' perception of satisfaction. Five physicians were tape recorded during their initial interviews with 52 adult patients. The patients were asked to complete the Medical Interview Satisfaction Scale, a 29-item instrument with a 7-point response scale. These interviews were transcribed, timed, coded, and analyzed with the use of the Computerized Language Analysis System. Selected variables of the language dimensions were entered as the predictor variables in a multiple regression, along with satisfaction scores as the dependent variables. Twenty-seven percent of the variance (p less than .01) in the satisfaction scores of initial interviews were explained by three aspects of a physician's language style: (a) use of silence or reaction time latency between speakers in an interview, (b) whether there was language reciprocity as determined through the reciprocal use of word-lists, and (c) the reflective use of interruptions within an interview. Considering the complexity of human communication, the fact that three variables were identified, which accounted for 27% of the variance in patients' satisfaction, is considered a substantial finding. 10.1177/016327879001300202