Verification of a clinical decision support system for the diagnosis of headache disorders based on patient–computer interactions: a multi-center study

Abstract

Background Although headache disorders are common, the current diagnostic approach is unsatisfactory. Previously, we designed a guideline-based clinical decision support system (CDSS 1.0) for diagnosing headache disorders. However, the system requires doctors to enter electronic information, which may limit widespread use. Methods In this study, we developed the updated CDSS 2.0, which handles clinical information acquisition via human–computer conversations conducted on personal mobile devices in an outpatient setting. We tested CDSS 2.0 at headache clinics in 16 hospitals in 14 provinces of China. Results Of the 653 patients recruited, 18.68% (122/652) were suspected by specialists to have secondary headaches. According to “red-flag” responses, all these participants were warned of potential secondary risks by CDSS 2.0. For the remaining 531 patients, we compared the diagnostic accuracy of assessments made using only electronic data firstly. In Comparison A, the system correctly recognized 115/129 (89.15%) cases of migraine without aura (MO), 32/32 (100%) cases of migraine with aura (MA), 10/10 (100%) cases of chronic migraine (CM), 77/95 (81.05%) cases of probable migraine (PM), 11/11 (100%) cases of infrequent episodic tension-type headache (iETTH), 36/45 (80.00%) cases of frequent episodic tension-type headache (fETTH), 23/25 (92.00%) cases of chronic tension-type headache (CTTH), 53/60 (88.33%) cases of probable tension-type headache (PTTH), 8/9 (88.89%) cases of cluster headache (CH), 5/5 (100%) cases of new daily persistent headache (NDPH), and 28/29 (96.55%) cases of medication overuse headache (MOH). In Comparison B, after combining outpatient medical records, the correct recognition rates of MO (76.03%), MA (96.15%), CM (90%), PM (75.29%), iETTH (88.89%), fETTH (72.73%), CTTH (95.65%), PTTH (79.66%), CH (77.78%), NDPH (80%), and MOH (84.85%) were still satisfactory. A patient satisfaction survey indicated that the conversational questionnaire was very well accepted, with high levels of satisfaction reported by 852 patients. Conclusions The CDSS 2.0 achieved high diagnostic accuracy for most primary and some secondary headaches. Human–computer conversation data were well integrated into the diagnostic process, and the system was well accepted by patients. The follow-up process and doctor–client interactions will be future areas of research for the development of CDSS for headaches.

New management strategies for primary headache disorders: Insights from P4 medicine

“…Recently, several machine learning models have been attempted to be developed for assisting with headache diagnosis [ 27 , 28 ]. A new guideline-based clinical decision support system (CDSS 2.0), developed by Han and his colleagues, is a good application for headache diagnosis based on doctor–patient interactions [ 27 ]. Clinical information is obtained through human-computer conversations conducted on a personal mobile device in an outpatient clinic.…”

Evaluating Headache and Facial Pain in a Headache Diagnostic Laboratory: Experiences from the Danish Headache Center

“…Rare primary and secondary headaches also need to be phenotyped as well as the effect of off-label treatment. The testing and use of digital diagnostic tools [ 49 , 50 , 51 ] and headache calendars would also be of interest in future studies. Imaging studies have been useful in illuminating the pathophysiology of migraine and may be a future biomarker for treatment response [ 52 ].…”