Triaging Patients With Artificial Intelligence for Respiratory Symptoms in Primary Care to Improve Patient Outcomes: A Retrospective Diagnostic Accuracy Study.

PURPOSE: Respiratory symptoms are the most common presenting complaint in primary care. Often these symptoms are self resolving, but they can indicate a severe illness. With increasing physician workload and health care costs, triaging patients before in-person consultations would be helpful, possibly offering low-risk patients other means of communication. The objective of this study was to train a machine learning model to triage patients with respiratory symptoms before visiting a primary care clinic and examine patient outcomes in the context of the triage. METHODS: We trained a machine learning model, using clinical features only available before a medical visit. Clinical text notes were extracted from 1,500 records for patients that received 1 of 7 International Classification of Diseases 10th Revision codes (J00, J10, JII, J15, J20, J44, J45). All primary care clinics in the Reykjavík area of Iceland were included. The model scored patients in 2 extrinsic data sets and divided them into 10 risk groups (higher values having greater risk). We analyzed selected outcomes in each group. RESULTS: Risk groups 1 through 5 consisted of younger patients with lower C-reactive protein values, re-evaluation rates in primary and emergency care, antibiotic prescription rates, chest x-ray (CXR) referrals, and CXRs with signs of pneumonia, compared with groups 6 through 10. Groups 1 through 5 had no CXRs with signs of pneumonia or diagnosis of pneumonia by a physician. CONCLUSIONS: The model triaged patients in line with expected outcomes. The model can reduce the number of CXR referrals by eliminating them in risk groups 1 through 5, thus decreasing clinically insignificant incidentaloma findings without input from clinicians.

Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy.

OBJECTIVE: Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction. DESIGN: A retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland. SETTING: Fifteen primary health care centers of the PHCCA. SUBJECTS: All patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses. MAIN OUTCOME MEASURES: Sensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier. RESULTS: The classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses. CONCLUSION: In a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction.KeypointsLittle is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.

[Effectiveness of an online cognitive behavioral therapy for insomnia]. / Árangur hugrænnar atferlismeðferðar við svefnleysi sem veitt er á netinu.

INTRODUCTION: Insomnia is a common health problem with serious mental and physical consequences as well as increased economical costs. The use of hypnotics in Iceland is immense in spite of cognitive behavioral therapy for insomnia (CBT-I) being recommended as the first choice treatment of chronic insomnia. To meet the needs of more individuals suffering from insomnia, online CBT-I was established at betrisvefn.is. The objective of this research was to evaluate the effectiveness of this internet-based CBT-I. MATERIAL AND METHODS: One hundred seventy-five users (mean age 46 y (18-79 y)) started a 6 week online intervention for insomnia. The drop-out rate was 29%, leaving a final sample of 125 users. The intervention is based on well-established face-to-face CBT-I. Sleep diaries were used to determine changes in sleep efficiency, sleep onset latency and wake after sleep onset. Treatment effects were assesed after 6 weeks of treatment and at the 6 week follow-up. RESULTS: Significant improvement was found in all main sleep variables except for 5% decrease in total sleep time (TST). Effects were sustained at 6 week follow-up and TST increased. The use of hypnotics decreased significantly. This form of treatment seems to suit its users very well and over 94% would recommend the treatment. CONCLUSION: Internet interventions for insomnia seem to have good potential. CBT-I will hopefully be offered as the first line treatment for chronic insomnia in Iceland instead of hypnotics as the availability of the CBT-I is growing. Thus, the burden on health care clinics might reduce along with the hypnotics use and the considerable costs of insomnia.