EXPLORING THE POTENTIALS OF PATIENT-GENERATED HEALTH DATA FOR THE TREATMENT OF DEPRESSION

Patient-generated health data (PGHD) enables healthcare professionals to get deeper insights into patients with depression, thus offering the opportunity to improve their treatment. However, due to the variety and methods for collecting PGHD, not all types are relevant for healthcare professionals in depression care. To identify relevant types of PGHD for the treatment of depression, we conducted a qualitative focus group study with 13 healthcare professionals and follow-up interviews. The study\u27s key findings include relevant identified PGHD concerning their collection effort. In addition, the results show a clear preference for PGHD that both have strong connections to depressive symptoms and use passive collection methods, such as sleep data and activity levels. With this article, we contribute to the usage of PGHD in clinical settings and thus create a better understanding of relevant types of PGHD for the treatment of depression

Predictors and outcomes in primary depression care (POKAL) – a research training group develops an innovative approach to collaborative care

BACKGROUND: The interdisciplinary research training group (POKAL) aims to improve care for patients with depression and multimorbidity in primary care. POKAL includes nine projects within the framework of the Chronic Care Model (CCM). In addition, POKAL will train young (mental) health professionals in research competences within primary care settings. POKAL will address specific challenges in diagnosis (reliability of diagnosis, ignoring suicidal risks), in treatment (insufficient patient involvement, highly fragmented care and inappropriate long-time anti-depressive medication) and in implementation of innovations (insufficient guideline adherence, use of irrelevant patient outcomes, ignoring relevant context factors) in primary depression care. METHODS: In 2021 POKAL started with a first group of 16 trainees in general practice (GPs), pharmacy, psychology, public health, informatics, etc. The program is scheduled for at least 6 years, so a second group of trainees starting in 2024 will also have three years of research-time. Experienced principal investigators (PIs) supervise all trainees in their specific projects. All projects refer to the CCM and focus on the diagnostic, therapeutic, and implementation challenges. RESULTS: The first cohort of the POKAL research training group will develop and test new depression-specific diagnostics (hermeneutical strategies, predicting models, screening for suicidal ideation), treatment (primary-care based psycho-education, modulating factors in depression monitoring, strategies of de-prescribing) and implementation in primary care (guideline implementation, use of patient-assessed data, identification of relevant context factors). Based on those results the second cohort of trainees and their PIs will run two major trials to proof innovations in primary care-based a) diagnostics and b) treatment for depression. CONCLUSION: The research and training programme POKAL aims to provide appropriate approaches for depression diagnosis and treatment in primary care

Lessons learned from applying established cut-off values of questionnaires to detect somatic symptom disorders in primary care: a cross-sectional study

IntroductionBased on two diagnostic accuracy studies in high-prevalence settings, two distinctly different combinations of cut-off values have been recommended to identify persons at risk for somatic symptom disorder (SSD) with the combination of the Patient-Health Questionnaire-15 (PHQ-15) and the Somatic Symptom Disorder—B Criteria Scale (SSD-12). We investigated whether the reported sensitivity and specificity of both recommended cut-off combinations are transferable to primary care.MethodsIn a cross-sectional study, 420 unselected adult primary care patients completed PHQ-15 and SSD-12. Patients scoring ≥9 and ≥ 23 (recommended cut-off combination #1) or ≥ 8 and ≥ 13 (recommended cut-off combination #2) were considered test-positive for SSD, respectively. To assess the validity of the reported sensitivity and specificity in different low- to high-prevalence settings, we compared correspondingly expected proportions of test positives to the proportion observed in our sample.ResultsBased on combination #1, 38 participants (9%) were found to be test positive, far fewer than expected, based on the reported values for sensitivity and specificity (expected minimum frequency 30% with a true prevalence ≥1%). This can only be explained by a lower sensitivity and higher specificity in primary care. For combination #2, 98 participants (23%) were test positive, a finding consistent with a true prevalence of SSD of 15% or lower.DiscussionOur analyzes strongly suggest that the sensitivity and specificity estimates reported for combination #1 are not applicable to unselected primary care patients and that the cut-off for the SSD (≥23) is too strict. Cut-off combination #2 seems more applicable but still needs to be tested in studies that compare screening findings by questionnaires with validated diagnostic interviews as reference standards in primary care populations

Insights on Patient-Generated Health Data in Healthcare: A Literature Review

Through the growing spread of eHealth applications, users are now able to easily generate vast amounts of personal, health data. However, to this date it is unclear what the role of patient-generated health data (PGHD) in healthcare is, based on an IS perspective. This promising source of personalized patient data holds the big opportunity of improving diagnosis and treatment of diseases. Therefore, we address this topic using a structured literature review. Based on an analysis of 131 papers, we provide insights into three major literature streams: (a) PGHD collection methods, (b) integration of PGHD into clinical workflows and (c) influence of PGHD on patient clinician interactions. Our findings present the current research on these three literature streams and highlight the benefits and challenges of PGHD. This paper contributes to the understanding of PGHD usage in healthcare from an IS viewpoint and provides a starting point for future IS research