Healthcare utilization patterns of individuals with depression after national policy to increase the mental health workforce in primary care: a data linkage study.

BACKGROUND: The deployment of the mental health nurse, an additional healthcare provider for individuals in need of mental healthcare in Dutch general practices, was expected to substitute treatments from general practitioners and providers in basic and specialized mental healthcare (psychologists, psychotherapists, psychiatrists, etc.). The goal of this study was to investigate the extent to which the degree of mental health nurse deployment in general practices is associated with healthcare utilization patterns of individuals with depression. METHODS: We combined national health insurers' claims data with electronic health records from general practices. Healthcare utilization patterns of individuals with depression between 2014 and 2019 (N = 31,873) were analysed. The changes in the proportion of individuals treated after depression onset were assessed in association with the degree of mental health nurse deployment in general practices. RESULTS: The proportion of individuals with depression treated by the GP, in basic and specialized mental healthcare was lower in individuals in practices with high mental health nurse deployment. While the association between mental health nurse deployment and consultation in basic mental healthcare was smaller for individuals who depleted their deductibles, the association was still significant. Treatment volume of general practitioners was also lower in practices with higher levels of mental health nurse deployment. CONCLUSION: Individuals receiving care at a general practice with a higher degree of mental health nurse deployment have lower odds of being treated by mental healthcare providers in other healthcare settings. More research is needed to evaluate to what extent substitution of care from specialized mental healthcare towards general practices might be associated with waiting times for specialized mental healthcare.

Healthcare utilization patterns for knee and hip osteoarthritis before and after changes in national health insurance coverage: a data linkage study from the Netherlands.

INTRODUCTION: Medical guidelines aim to stimulate stepped care for knee and hip osteoarthritis, redirecting treatments from hospitals to primary care. In the Netherlands, this development was supported by changing health insurance coverage for physio/exercise therapy. The aim of this study was to evaluate healthcare utilization patterns before and after health changes in health insurance coverage. METHOD: We analyzed electronic health records and claims data from patients with osteoarthritis in the knee (N = 32,091) and hip (N = 16,313). Changes between 2013 and 2019 in the proportion of patients treated by the general practitioner, physio/exercise therapist or orthopedic surgeon within 6 months after onset were assessed. RESULTS: Joint replacement surgeries decreased for knee (OR 0.47 [0.41-0.54]) and hip (OR 0.81 [0.71-0.93]) osteoarthritis between 2013-2019. The use of physio/exercise therapy increased (knee: OR 1.38 [1.24-1.53], hip: OR 1.26 [1.08-1.47]). However, the proportion treated by a physio/exercise therapist decreased for patients that had not depleted their annual deductibles (knee: OR 0.86 [0.79 - 0.94], hip: OR 0.90 [0.79 - 1.02]). This might be affected by the inclusion of physio/exercise therapy in basic health insurance in 2018. CONCLUSION: We have found a shift from hospitals to primary care in knee and hip osteoarthritis care. However, the use of physio/exercise therapy declined after changes in insurance coverage for patients that had not depleted their deductibles.

Detection of primary Sjögren's syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning.

BACKGROUND: Primary Sjögren's Syndrome (pSS) is a rare autoimmune disease that is difficult to diagnose due to a variety of clinical presentations, resulting in misdiagnosis and late referral to specialists. To improve early-stage disease recognition, this study aimed to develop an algorithm to identify possible pSS patients in primary care. We built a machine learning algorithm which was based on combined healthcare data as a first step towards a clinical decision support system. METHOD: Routine healthcare data, consisting of primary care electronic health records (EHRs) data and hospital claims data (HCD), were linked on patient level and consisted of 1411 pSS and 929,179 non-pSS patients. Logistic regression (LR) and random forest (RF) models were used to classify patients using age, gender, diseases and symptoms, prescriptions and GP visits. RESULTS: The LR and RF models had an AUC of 0.82 and 0.84, respectively. Many actual pSS patients were found (sensitivity LR = 72.3%, RF = 70.1%), specificity was 74.0% (LR) and 77.9% (RF) and the negative predictive value was 99.9% for both models. However, most patients classified as pSS patients did not have a diagnosis of pSS in secondary care (positive predictive value LR = 0.4%, RF = 0.5%). CONCLUSION: This is the first study to use machine learning to classify patients with pSS in primary care using GP EHR data. Our algorithm has the potential to support the early recognition of pSS in primary care and should be validated and optimized in clinical practice. To further enhance the algorithm in detecting pSS in primary care, we suggest it is improved by working with experienced clinicians.