An online experiment to assess bias in professional medical coding.

BackgroundMultiple studies have documented bias in medical decision making, but no studies have examined whether this bias extends to medical coding practices. Medical coding is foundational to the US health care enterprise. We evaluate whether bias based on patient characteristics influences specific coding practices of professional medical coders.MethodsThis is an online experimental study of members of a national professional medical coding organization. Participants were randomly assigned a set of six clinical scenarios reflecting common medical conditions and asked to report encounter level of service codes for these clinical scenarios. Clinical scenarios differed by patient demographics (race, age, gender, ability) or social context (food insecurity, housing security) but were otherwise identical. We estimated Ordinary Least Squares regression models to evaluate differences in outcome average visit level of service by patient demographic characteristics described in the clinical scenarios; we adjusted for coders' age, gender, race, and years of coding experience.ResultsThe final analytic sample included 586 respondents who coded at least one clinical scenario. Higher mean level of service was assigned to clinical scenarios describing seniors compared to middle-aged patients in two otherwise identical scenarios, one a patient with type II diabetes mellitus (Coef: 0.28, SE: 0.15) and the other with rheumatoid arthritis (Coef: 0.30, SE: 0.13). Charts describing women were assigned lower level of service than men in patients with asthma exacerbation (Coef: -0.25, SE: 0.13) and rheumatoid arthritis (Coef: -0.20, SE: 0.12). There were no other significant differences in mean complexity score by patient demographics or social needs.ConclusionWe found limited evidence of bias in professional medical coding practice by patient age and gender, though findings were inconsistent across medical conditions. Low levels of observed bias may reflect medical coding workflow and training practices. Future research is needed to better understand bias in coding and to identify effective and generalizable bias prevention practices

Email for communicating results of diagnostic medical investigations to patients

<p>Background: As medical care becomes more complex and the ability to test for conditions grows, pressure on healthcare providers to convey increasing volumes of test results to patients is driving investigation of alternative technological solutions for their delivery. This review addresses the use of email for communicating results of diagnostic medical investigations to patients.</p> <p>Objectives: To assess the effects of using email for communicating results of diagnostic medical investigations to patients, compared to SMS/ text messaging, telephone communication or usual care, on outcomes, including harms, for health professionals, patients and caregivers, and health services.</p> <p>Search methods: We searched: the Cochrane Consumers and Communication Review Group Specialised Register, Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library, Issue 1 2010), MEDLINE (OvidSP) (1950 to January 2010), EMBASE (OvidSP) (1980 to January 2010), PsycINFO (OvidSP) (1967 to January 2010), CINAHL (EbscoHOST) (1982 to February 2010), and ERIC (CSA) (1965 to January 2010). We searched grey literature: theses/dissertation repositories, trials registers and Google Scholar (searched July 2010). We used additional search methods: examining reference lists and contacting authors.</p> <p>Selection criteria: Randomised controlled trials, quasi-randomised trials, controlled before and after studies and interrupted time series studies of interventions using email for communicating results of any diagnostic medical investigations to patients, and taking the form of 1) unsecured email 2) secure email or 3) web messaging. All healthcare professionals, patients and caregivers in all settings were considered.</p> <p>Data collection and analysis: Two review authors independently assessed the titles and abstracts of retrieved citations. No studies were identified for inclusion. Consequently, no data collection or analysis was possible.</p> <p>Main results: No studies met the inclusion criteria, therefore there are no results to report on the use of email for communicating results of diagnostic medical investigations to patients.</p> <p>Authors' conclusions: In the absence of included studies, we can draw no conclusions on the effects of using email for communicating results of diagnostic medical investigations to patients, and thus no recommendations for practice can be stipulated. Further well-designed research should be conducted to inform practice and policy for communicating patient results via email, as this is a developing area.</p&gt

Grid multi-category response logistic models.

BackgroundMulti-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations.MethodsThis paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation.ResultsSimulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models.ConclusionsThe grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models

Computer-assisted versus oral-and-written dietary history taking for diabetes mellitus

Background: Diabetes is a chronic illness characterised by insulin resistance or deficiency, resulting in elevated glycosylated haemoglobin A1c (HbA1c) levels. Diet and adherence to dietary advice is associated with lower HbA1c levels and control of disease. Dietary history may be an effective clinical tool for diabetes management and has traditionally been taken by oral-and-written methods, although it can also be collected using computer-assisted history taking systems (CAHTS). Although CAHTS were first described in the 1960s, there remains uncertainty about the impact of these methods on dietary history collection, clinical care and patient outcomes such as quality of life. Objectives: To assess the effects of computer-assisted versus oral-and-written dietary history taking on patient outcomes for diabetes mellitus. Search methods: We searched The Cochrane Library (issue 6, 2011), MEDLINE (January 1985 to June 2011), EMBASE (January 1980 to June 2011) and CINAHL (January 1981 to June 2011). Reference lists of obtained articles were also pursued further and no limits were imposed on languages and publication status. Selection criteria: Randomised controlled trials of computer-assisted versus oral-and-written history taking in patients with diabetes mellitus. Data collection and analysis: Two authors independently scanned the title and abstract of retrieved articles. Potentially relevant articles were investigated as full text. Studies that met the inclusion criteria were abstracted for relevant population and intervention characteristics with any disagreements resolved by discussion, or by a third party. Risk of bias was similarly assessed independently. Main results: Of the 2991 studies retrieved, only one study with 38 study participants compared the two methods of history taking over a total of eight weeks. The authors found that as patients became increasingly familiar with using CAHTS, the correlation between patients' food records and computer assessments improved. Reported fat intake decreased in the control group and increased when queried by the computer. The effect of the intervention on the management of diabetes mellitus and blood glucose levels was not reported. Risk of bias was considered moderate for this study. Authors' conclusions: Based on one small study judged to be of moderate risk of bias, we tentatively conclude that CAHTS may be well received by study participants and potentially offer time saving in practice. However, more robust studies with larger sample sizes are needed to confirm these. We cannot draw on any conclusions in relation to any other clinical outcomes at this stage

Reliability of Quality Assessments in Research Synthesis: Securing the Highest Quality Bioinformation for HIT.

Current trends in bio-medicine include research synthesis and dissemination of bioinformation by means of health (bio) information technology (H[b] IT). Research must secure the validity and reliability of assessment tools to quantify research quality in the pursuit of the best available evidence. Our concerted work in this domain led to the revision of three instruments for that purpose, including the stringent characterization of inter-rater reliability and coefficient of agreement. It is timely and critical to advance the methodological development of the science of research synthesis by strengthening the reliability of existing measure of research quality in order to ensure H[b] IT efficacy and effectiveness

The journals of importance to UK clinicians: A questionnaire survey of surgeons

Background: Peer-reviewed journals are seen as a major vehicle in the transmission of research findings to clinicians. Perspectives on the importance of individual journals vary and the use of impact factors to assess research is criticised. Other surveys of clinicians suggest a few key journals within a specialty, and sub-specialties, are widely read. Journals with high impact factors are not always widely read or perceived as important. In order to determine whether UK surgeons consider peer-reviewed journals to be important information sources and which journals they read and consider important to inform their clinical practice, we conducted a postal questionnaire survey and then compared the findings with those from a survey of US surgeons. Methods: A questionnaire survey sent to 2,660 UK surgeons asked which information sources they considered to be important and which peer-reviewed journals they read, and perceived as important, to inform their clinical practice. Comparisons were made with numbers of UK NHSfunded surgery publications, journal impact factors and other similar surveys. Results: Peer-reviewed journals were considered to be the second most important information source for UK surgeons. A mode of four journals read was found with academics reading more than non-academics. Two journals, the BMJ and the Annals of the Royal College of Surgeons of England, are prominent across all sub-specialties and others within sub-specialties. The British Journal of Surgery plays a key role within three sub-specialties. UK journals are generally preferred and readership patterns are influenced by membership journals. Some of the journals viewed by surgeons as being most important, for example the Annals of the Royal College of Surgeons of England, do not have high impact factors. Conclusion: Combining the findings from this study with comparable studies highlights the importance of national journals and of membership journals. Our study also illustrates the complexity of the link between the impact factors of journals and the importance of the journals to clinicians. This analysis potentially provides an additional basis on which to assess the role of different journals, and the published output from research

Accuracy of medical billing data against the electronic health record in the measurement of colorectal cancer screening rates.

ObjectiveMedical billing data are an attractive source of secondary analysis because of their ease of use and potential to answer population-health questions with statistical power. Although these datasets have known susceptibilities to biases, the degree to which they can distort the assessment of quality measures such as colorectal cancer screening rates are not widely appreciated, nor are their causes and possible solutions.MethodsUsing a billing code database derived from our institution's electronic health records, we estimated the colorectal cancer screening rate of average-risk patients aged 50-74 years seen in primary care or gastroenterology clinic in 2016-2017. 200 records (150 unscreened, 50 screened) were sampled to quantify the accuracy against manual review.ResultsOut of 4611 patients, an analysis of billing data suggested a 61% screening rate, an estimate that matches the estimate by the Centers for Disease Control. Manual review revealed a positive predictive value of 96% (86%-100%), negative predictive value of 21% (15%-29%) and a corrected screening rate of 85% (81%-90%). Most false negatives occurred due to examinations performed outside the scope of the database-both within and outside of our institution-but 21% of false negatives fell within the database's scope. False positives occurred due to incomplete examinations and inadequate bowel preparation. Reasons for screening failure include ordered but incomplete examinations (48%), lack of or incorrect documentation by primary care (29%) including incorrect screening intervals (13%) and patients declining screening (13%).ConclusionsBilling databases are prone to substantial bias that may go undetected even in the presence of confirmatory external estimates. Caution is recommended when performing population-level inference from these data. We propose several solutions to improve the use of these data for the assessment of healthcare quality

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064