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Identifying patients with medically unexplained physical symptoms in electronic medical records in primary care: a validation study.

ABSTRACT: BACKGROUND: When medically unexplained physical symptoms (MUPS) become persistent, it may have major implications for the patient, the general practitioner (GP) and for society.Early identification of patients with MUPS in electronic medical records (EMRs) might contribute to prevention of persistent MUPS by creating awareness among GPs and providing an opportunity to start stepped care management. However, procedures for identification of patients with MUPS in EMRs are not well established yet. In this validation study we explore the test characteristics of an EMR screening method to identify patients with MUPS. METHODS: The EMR screening method consists of three steps. First, all patients ? 18 years were included when they had five or more contacts in the last 12 months. Second, patients with known chronic conditions were excluded. Finally, patients were included with a MUPS syndrome or when they had three or more complaints suggestive for MUPS. We compared the results of the EMR screening method with scores on the Patient Health Questionnaire-15 (PHQ-15), which we used as reference test. We calculated test characteristics for various cut-off points. RESULTS: From the 1223 patients in our dataset who completed the PHQ-15, 609 (49/8%) scored ? 5 on the PHQ-15. The EMR screening method detected 131/1223 (10.7%) as patients with MUPS. Of those, 102 (77.9%) scored ? 5 on the PHQ-15 and 53 (40.5%) scored ? 10. When compared with the PHQ-15 cut-off point ? 10, sensitivity and specificity were 0.30 and 0.93 and positive and negative predictive values were 0.40 and 0.89, respectively. CONCLUSIONS: The EMR screening method to identify patients with MUPS has a high specificity. However, many potential MUPS patients will be missed. Before using this method as a screening instrument for selecting patients who might benefit from structured care, its sensitivity needs to be improved while maintaining its specificity.

SUBMITTER: den Boeft M

PROVIDER: S-EPMC4052805 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identifying patients with medically unexplained physical symptoms in electronic medical records in primary care: a validation study.

den Boeft Madelon M van der Wouden Johannes C JC Rydell-Lexmond Trudie R TR de Wit Niek J NJ van der Horst Henriëtte E HE Numans Mattijs E ME

BMC family practice 20140605

<h4>Background</h4>When medically unexplained physical symptoms (MUPS) become persistent, it may have major implications for the patient, the general practitioner (GP) and for society.Early identification of patients with MUPS in electronic medical records (EMRs) might contribute to prevention of persistent MUPS by creating awareness among GPs and providing an opportunity to start stepped care management. However, procedures for identification of patients with MUPS in EMRs are not well establish ...[more]

PMID: 24903850

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Project description:BackgroundPatients with medically unexplained physical symptoms (MUPS) are prevalent 25-50% in general and specialist care. Medical specialists and residents often find patients without underlying pathology difficult to deal with, whereas patients sometimes don't feel understood. We developed an evidence-based communication training, aimed to improve specialists' interviewing, information-giving and planning skills in MUPS consultations, and tested its effectiveness.MethodsThe intervention group in this multi-center randomized controlled trial received a 14-hour training program to which experiential learning and feedback were essential. Using techniques from Cognitive Behavioral Therapy, they were stimulated to seek interrelating factors (symptoms, cognitions, emotions, behavior, and social environment) that reinforced a patient's symptoms. They were taught to explain MUPS understandably, reassure patients effectively and avoid unnecessary diagnostic testing. Before and after the intervention training, specialists videotaped a total of six consultations with different MUPS patients. These were evaluated to assess doctors' MUPS-focused communicating skills using an adapted version of the Four Habit Coding Scheme on five-point Likert scales. Participants evaluated the training by self-report on three-point Likert scales. Doctors in the control group received training after completion of the study.Results123 doctors (40% specialists, 60% residents) and 478 MUPS patients from 11 specialties were included; 98 doctors completed the study (80%) and 449 videotaped consultations were assessed. Trained doctors interviewed patients more effectively than untrained ones (p < 0.001), summarized information in a more patient-centered way (p = 0.001), and better explained MUPS and the role of perpetuating factors (p < 0.05). No effects on planning skills were found. On a 3-point scale the training was evaluated with 2.79.ConclusionMUPS-focused communication training increases the interviewing and information-giving skills of medical specialists. We recommend that the training is incorporated in postgraduate education for medical specialists and residents who frequently encounter patients with MUPS.Trial registrationDutch Trial Registration NTR2612.

Project description:To conduct a pilot trial of a primary care Symptoms Clinic for patients with medically unexplained symptoms and evaluate recruitment and retention, and acceptability of the intervention and to estimate potential treatment effects for a full trial.Randomised parallel group pilot trial.Primary care in one locality.Primary care database and postal questionnaire were used to identify patients with multiple specialist referrals and multiple physical symptoms unlikely to be explained by disease.General practitioner (GP) with special interest 'Symptoms Clinic' + usual care versus usual care alone. The Symptoms Clinic comprised one long (1 h) and three short (20 min) appointments.Number of patients identified and recruited; acceptability of the intervention (items from Client Satisfaction Questionnaire and interview); Medical Outcomes Survey Short Form 12 (SF-12) physical component summary.Automated blocked randomisation accessed by telephone.None. NUMBERS RANDOMIZED: 16 to intervention and 16 to usual care alone.72 patients, from seven GP practices, had repeated specialist referrals and a Patient Health Questionnaire (PHQ)-15 score of ?10 indicating a high probability of medically unexplained symptoms. 15 were ineligible and 25 declined to participate. NUMBERS ANALYSED: 26 patients; two patients randomised to the intervention group were incorrectly included, three patients in the intervention group and one control did not complete outcome measures.Most patients randomised to the Symptoms Clinic found the intervention acceptable: eight out of 11 reported the intervention helped them to deal with their problems. The mean difference between groups in SF-12 physical component summary, adjusted for baseline, was 3.8 points (SD 6).No observed harms.Patients with multiple medically unexplained symptoms can be systematically identified in primary care; a randomised trial comparing the Symptoms Clinic with usual care is feasible and has the potential to show clinically meaningful benefit.ISRCTN63083469.

Project description:BackgroundMusculoskeletal (MSK) conditions are a common presentation in primary care. This study sought to determine the prevalence of MSK conditions in primary care in Ontario and to validate the extent to which health administrative date billing codes accurately represent MSK diagnoses.MethodsDe-identified electronic medical records (EMR) from the DELPHI database in southwestern Ontario, which contains 2493 patients (55.6% female, mean age 50.3 years (SD = 22.2)) and 21,964 encounters (July 1, 2006-June 30, 2010) were used for the analyses. Outcomes included: validation measures of agreement between International Classification of Diseases (ICD-9) diagnostic codes (health administrative data) and International Classification of Primary Care (ICPC) diagnoses defined as the reference standard, time to first ICD-9 code, prevalence, and healthcare utilization.ResultsThere were 2940 true positive MSK encounters with primary care practitioners for 998 patients. Performance of the ICD-9 diagnostic codes included sensitivity = 76.5%, specificity = 95.2%, PPV = 94.6%, and NPV = 78.7%, compared to the ICPC reference standard. The majority of 998 patients were coded with both an ICPC and ICD-9 MSK code at their first or second encounter (67.4%). However, 23.5% of patients with the ICPC reference standard MSK were never coded with ICD-9. Four-year prevalence of MSK was 52.3% and varied by age (4.5% 0-17 years, 20.1% 18-44, 42.7% 45-64, and 32.7% 65+). Patients at MSK encounters had a higher number of: investigations (17.9% compared to 9.1%, p < .0001); referrals (17.6% compared to 14.3%, p < .0001); and prescriptions for opioids (17.2% compared to 5.3%, p < .0001).ConclusionsThis study determined the prevalence of musculoskeletal conditions in primary care in Ontario using a reference standard definition. The study highlighted the value of using primary care ICPC codes to validate a definition for musculoskeletal conditions. Health administrative data can be used to ascertain the presence of musculoskeletal conditions; however, ICD-9 codes may underrepresent the prevalence of MSK conditions.

Project description:BACKGROUND:Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms. OBJECTIVE:The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records. METHODS:CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration. Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation. RESULTS:The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status. CONCLUSIONS:We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations.

Dataset Information

Identifying patients with medically unexplained physical symptoms in electronic medical records in primary care: a validation study.

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Identifying patients with medically unexplained physical symptoms in electronic medical records in primary care: a validation study.

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