Presentation of medically unexplained physical symptoms (MUPS) is a common phenomenon in primary care. Of all primary care encounters, in up to a third the symptoms presented by the patient remain unexplained [1, 2]. In specialist care, these figures may even be higher, depending on the specialty . Although MUPS become persistent in only a minority (2.5%) of patients, the burden of persistent MUPS is high for both patients and doctors and for society . Patients are functionally impaired and may feel that they are not taken seriously by their general practitioner (GP) [5–7]. Furthermore, the doctor-patient relationship is often troubled and many GPs indicate that they find these patients difficult to manage [8, 9]. Also persistent MUPS may lead to high and inadequate health care utilization and high associated costs [10–12].
Early identification of patients with a higher risk of developing persistent MUPS in routine electronic medical records (EMRs) could create an opportunity for proactive and structured care, taking into account the severity of MUPS, coordinated by GPs. Awareness among GPs of their population at risk could result in more attention during consultations or in offering effective interventions like cognitive behaviour therapy at an earlier stage if appropriate . The advantage of using EMRs is that the data are directly available and no additional data collection is needed, which saves time consuming logistical procedures. Furthermore it provides a quick overview of a population at risk.
Early identification in EMRs proved to be feasible and effective for other risk populations, like patients with type 2 diabetes, cardiovascular risks and frail elderly [14–16] as well as for preventive health care . Also Tian et al. developed an applicable EMR algorithm to identify patients with chronic pain . However, identifying patients with MUPS is not an easy task as there is no generally accepted procedure available. Although some MUPS characteristics, like frequent consultation and referral rate, can be obtained from EMRs, there is no international classification of primary care (ICPC) code available that identifies the combination of symptoms that characterise MUPS of various MUPS subgroups.
Morriss et al. developed an EMR model that estimates the prevalence of MUPS. However, they concluded that the model is not useful for screening purposes due to a low sensitivity . Various other methods for MUPS screening have been developed and studied. Kroenke et al. showed in their validation study that the self-administered Patient Health Questionnaire-15 (PHQ-15) could be used for screening somatisation and somatic symptom severity including MUPS . However, the PHQ-15 can not be easily obtained from EMRs. Verhaak et al. used criteria composed by Robbins et al. to estimate the prevalence of persistent MUPS, but in their study it is about the patients who already suffer from persistent MUPS and not about the patients at risk [4, 21].
In 2010, a cross-sectional study focusing on the prevalence of MUPS was conducted in the Utrecht Health Project. Patients with MUPS were identified using EMR data in three subsequent selection steps. In our current study we aim to validate this EMR screening method to identify MUPS patients by comparing it to the commonly used and validated PHQ-15.