Quality of care in patients with hypertension: a retrospective cohort study of primary care routine data in Germany.

BACKGROUND: Hypertension is a leading cause of morbidity and mortality if not properly managed. Primary care has a major impact on these outcomes if its strengths, such as continuity of care, are deployed wisely. The analysis aimed to evaluate the quality of care for newly diagnosed hypertension in routine primary care data. METHODS: In the retrospective cohort study, routine data (from 2016 to 2022) from eight primary care practices in Germany were exported in anonymized form directly from the electronic health record (EHR) systems and processed for this analysis. The analysis focused on five established quality indicators for the care of patients who have been recently diagnosed with hypertension. RESULTS: A total of 30,691 patients were treated in the participating practices, 2,507 of whom have recently been diagnosed with hypertension. Prior to the pandemic outbreak, 19% of hypertensive patients had blood pressure above 140/90 mmHg and 68% received drug therapy (n = 1,372). After the pandemic outbreak, the proportion of patients with measured blood pressure increased from 63 to 87%, while the other four indicators remained relatively stable. Up to 80% of the total variation of the quality indicators could be explained by individual practices. CONCLUSION: For the majority of patients, diagnostic procedures are not used to the extent recommended by guidelines. The analysis showed that quality indicators for outpatient care could be mapped onto the basis of routine data. The results could easily be reported to the practices in order to optimize the quality of care.

Identifying Existing Evidence to Potentially Develop a Machine Learning Diagnostic Algorithm for Cough in Primary Care Settings: Scoping Review.

BACKGROUND: Primary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined. OBJECTIVE: Cough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice. METHODS: The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor. RESULTS: In total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent. CONCLUSIONS: Reliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning-based diagnostic algorithm in a meaningful way.

Psychometric properties of the GP-Patient Relationship Questionnaire as an instrument for quality improvement in Germany.

INTRODUCTION: Since the patient-doctor relationship (PDR) plays a crucial role in patient primary health care and management, the evaluation of the PDR from both, the patients' and primary care physicians', perspectives is an important approach to improve the quality of primary care. However, although there are a variety of questionnaires surveying the patients' view of the PDR, only few consider the physicians' perspective. The purpose of this study was to develop a questionnaire that adds the physicians' perspective to an existing PDR instrument and thus enables a mutual assessment of the relationship. METHODS: This study was embedded in a German project aiming at enhancing the adherence in patients with hypertension. Patients and primary care physicians were asked to complete a questionnaire concerning their PDR. The Patient-Doctor Relationship Questionnaire (PDRQ-9) was used to assess the patients' perspective. For the physicians, the PDRQ-9 items were adapted to the physicians' perspective resulting in the GP-Patient Relationship Questionnaire (GPPR). The Helping Alliance Questionnaire (HAQ) was used for external validation. RESULTS: A total of 60 physician questionnaires and 50 matched pairs of questionnaires from physicians and patients were included in the analyses. Within the analysis, notable deviations were found for one item, nevertheless the calculated Cronbach's α of 0.89 showed a high internal consistency of the 9-item questionnaire. External validation showed a high correlation of the GPPR with the HAQ, confirming the good psychometric properties demonstrated for the overall instrument. DISCUSSION: Overall, this initial validation study revealed good psychometric properties of the GPPR. A retest will be performed in the course of the overall project to confirm the reliability of the newly developed questionnaire. CONCLUSION: A new questionnaire to assess the physicians' perspectives on the PDR was successfully developed to improve adherence-dependent processes of quality improving in primary care.