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Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.

ABSTRACT: Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.

SUBMITTER: Cadieux G

PROVIDER: S-EPMC3378465 | biostudies-other | 2012 Mar

REPOSITORIES: biostudies-other

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Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.

Cadieux Geneviève G Buckeridge David L DL Jacques André A Libman Michael M Dendukuri Nandini N Tamblyn Robyn R

BMC public health 20120308

<h4>Background</h4>Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, ...[more]

PMID: 22397597

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Project description:Community clinics offer potential for timelier outbreak detection and monitoring than emergency departments. However, the accuracy of syndrome definitions used in surveillance has never been evaluated in community settings. This study's objective was to assess the accuracy of syndrome definitions based on diagnostic codes in physician claims for identifying 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory including influenza-like illness) in community clinics.We selected a random sample of 3,600 community-based primary care physicians who practiced in the fee-for-service system in the province of Quebec, Canada in 2005-2007. We randomly selected 10 visits per physician from their claims, stratifying on syndrome type and presence, diagnosis, and month. Double-blinded chart reviews were conducted by telephone with consenting physicians to obtain information on patient diagnoses for each sampled visit. The sensitivity, specificity, and positive predictive value (PPV) of physician claims were estimated by comparison to chart review.1,098 (30.5%) physicians completed the chart review. A chart entry on the date of the corresponding claim was found for 10,529 (95.9%) visits. The sensitivity of syndrome definitions based on diagnostic codes in physician claims was low, ranging from 0.11 (fever) to 0.44 (respiratory), the specificity was high, and the PPV was moderate to high, ranging from 0.59 (fever) to 0.85 (respiratory). We found that rarely used diagnostic codes had a higher probability of being false-positives, and that more commonly used diagnostic codes had a higher PPV.Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate.

Project description:BackgroundMost asthma diagnoses and patient care take place in primary care settings. Electronic medical records (EMRs) offer an opportunity to utilize technology to improve asthma diagnosis and care. The purpose of this study was to create and validate separate case definitions for suspected and confirmed asthma in primary care EMRs, to enable surveillance, benchmarking, and quality improvement in primary care settings. The objective of this study was to develop a case definition for suspected and confirmed asthma for use in a primary care sentinel surveillance system.MethodsA single chart abstractor conducted a manual audit of 776 randomly selected patient charts from an academic primary care practice EMR in Kingston, Ontario. Following the single chart abstractor classification, a consensus on chart classification as "not asthma", "suspected asthma", or "confirmed asthma" was achieved between the abstractor, a family physician, and a respirologist using Canadian Thoracic Society (CTS) criteria. Case definition algorithms based on billing codes, clinical data elements and medications were applied to the site's Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data for the same charts and compared to abstractor classifications to determine each algorithm's measurement properties.ResultsThe prevalence of suspected and confirmed asthma were 7.3% (n = 54) and 2.4% (n = 18), respectively. None of the proposed case definitions could differentiate between suspected and confirmed asthma. One algorithm consisting of billing, clinical, and medication elements had the highest Youden's Index for either suspected or confirmed asthma. The algorithm had a sensitivity of 81%, a specificity of 96%, positive predictive value of 71%, negative predictive value of 98%, and a Youden's Index of 0.77 for combined suspected or confirmed asthma cases.ConclusionAn EMR case definition for suspected or confirmed adult asthma has been validated for use in CPCSSN. Implementation of this case definition will enable the development of a surveillance electronic tool (eTool) for adult asthma that can foster quality improvement.

Project description:BackgroundMost endocrinologists encounter patients who are dissatisfied with their current hypothyroidism therapy and request combination therapy with either liothyronine (LT3) or thyroid extract.MethodsA survey of American Thyroid Association members was conducted in 2017. Respondents were presented with 13 scenarios describing patients with hypothyroidism and were asked to choose among six therapeutic options. The index patient was satisfied taking levothyroxine (LT4) therapy. Twelve variations introduced parameters that potentially provide reasons for considering combination therapy (presence of symptoms, low serum triiodothyronine concentration, documentation of deiodinase polymorphisms). Therapeutic options included (i) continuing LT4, (ii) increasing LT4, (iii) adding LT3 to a reduced LT4 dose, (iv) adding LT3 to the current LT4 dose, (v) replacing LT4 with thyroid extract, and (vi) replacing LT4 with LT3. Repeated-measures logistic regression analysis was performed to examine both the prescribing of LT4 (options i and ii) versus all other therapies and the choice of continuing LT4 (option i) versus either increasing LT4 (option ii), adding LT3 (options iii and iv), or replacing LT4 with thyroid extract or LT3 (options v and vi).ResultsOf the 389 survey respondents, 363 physicians prescribed therapy for hypothyroidism. For the index patient, 98% of physicians continued current LT4 therapy. However, as the patient scenario incorporated other patient characteristics, physicians opted to increase LT4 dose or prescribe other therapies. The tendency to prescribe alternative therapies was powerfully increased by patient symptoms (odds ratio = 25.6 [confidence interval 9-73], p < 0.0001). Older age and the presence of a comorbidity reduced the likelihood that an alternative therapy was prescribed (p = 0.0002 and <0.0001, respectively). All other characteristics, except athyreotic status, patient sex, and body mass index, significantly increased the likelihood that alternative therapies would be prescribed in multivariate analyses (p < 0.0001).ConclusionsEven with the acknowledged limitations of survey methodology, this analysis appears to show a marked increase in the willingness of physicians to prescribe combination therapy in specific circumstances. If current prescribing patterns do incorporate the use of therapies other than LT4, there is a critical need for more research into the benefits and risks of these therapies.

Dataset Information

Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.

Publications

Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.

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