Ketan K. Mane

Next generation massively parallel sequencing of targeted exomes to identify genetic mutations in primary ciliary dyskinesia: implications for application to clinical testing

Purpose: Advances in genetic sequencing technology have the potential to enhance testing for genes associated with genetically heterogeneous clinical syndromes, such as primary ciliary dyskinesia. The objective of this study was to investigate the performance characteristics of exon-capture technology coupled with massively parallel sequencing for clinical diagnostic evaluation.Methods: We performed a pilot study of four individuals with a variety of previously identified primary ciliary dyskinesia mutations. We designed a custom array (NimbleGen) to capture 2089 exons from 79 genes associated with primary ciliary dyskinesia or ciliary function and sequenced the enriched material using the GS FLX Titanium (Roche 454) platform. Bioinformatics analysis was performed in a blinded fashion in an attempt to detect the previously identified mutations and validate the process.Results: Three of three substitution mutations and one of three small insertion/deletion mutations were readily identified using this methodology. One small insertion mutation was clearly observed after adjusting the bioinformatics handling of previously described SNPs. This process failed to detect two known mutations: one single-nucleotide insertion and a whole-exon deletion. Additional retrospective bioinformatics analysis revealed strong sequence-based evidence for the insertion but failed to detect the whole-exon deletion. Numerous other variants were also detected, which may represent potential genetic modifiers of the primary ciliary dyskinesia phenotype.Conclusions: We conclude that massively parallel sequencing has considerable potential for both research and clinical diagnostics, but further development is required before widespread adoption in a clinical setting.

VisualDecisionLinc: A visual analytics approach for comparative effectiveness-based clinical decision support in psychiatry

Comparative Effectiveness Research (CER) is designed to provide research evidence on the effectiveness and risks of different therapeutic options on the basis of data compiled from subpopulations of patients with similar medical conditions. Electronic Health Record (EHR) system contain large volumes of patient data that could be used for CER, but the data contained in EHR system are typically accessible only in formats that are not conducive to rapid synthesis and interpretation of therapeutic outcomes. In the time-pressured clinical setting, clinicians faced with large amounts of patient data in formats that are not readily interpretable often feel information overload. Decision support tools that enable rapid access at the point of care to aggregate data on the most effective therapeutic outcomes derived from CER would greatly aid the clinical decision-making process and individualize patient care. In this manuscript, we highlight the role that visual analytics can play in CER-based clinical decision support. We developed a VisualDecisionLinc (VDL) tool prototype that uses visual analytics to provide summarized CER-derived data views to facilitate rapid interpretation of large amounts of data. We highlight the flexibility that visual analytics offers to gain an overview of therapeutic options and outcomes and if needed, to instantly customize the evidence to the needs of the patient or clinician. The VDL tool uses visual analytics to help the clinician evaluate and understand the effectiveness and risk of different therapeutic options for different subpopulations of patients.

Patient electronic health data-driven approach to clinical decision support.

This article presents a novel visual analytics (VA)‐based clinical decision support (CDS) tool prototype that was designed as a collaborative work between Renaissance Computing Institute and Duke University. Using Major Depressive Disorder data from MindLinc electronic health record system at Duke, the CDS tool shows an approach to leverage data from comparative population (patients with similar medical profile) to enhance a clinicians’ decision making process at the point of care. The initial work is being extended in collaboration with the University of North Carolina CTSA to address the key challenges of CDS, as well as to show the use of VA to derive insight from large volumes of Electronic Health Record patient data. Clin Trans Sci 2011; Volume 4: 369–371

Personalization is not a panacea: balancing serendipity and personalization in medical news content delivery

Personalization is viewed as a potential solution to the information overload problem. In contrast, serendipity is a natural part of human information seeking process that can lead to unexpected and useful discoveries. It appears both serendipity and personalization are important. However, it remains unclear how these modes of interaction impact content retrieval and consumption. To empirically analyze the influence of personalization and serendipity on information retrieval, a medical news information system named MedSIFTER was developed. The system can personalize the presentation of news articles based on users interest profiles. Using a control variable, built into the system, MeSIFTERs personalization level can be modulated, ranging from high to low (or zero). In this study, based on MedlinePlus as main information source, three different system modalities were compared (zero, low, and high levels of personalization). The experimental analysis engaged three different user groups, over a four week period. Strong evidence of serendipity was found across all three user groups, independent of the level of personalization. Users also appeared to be uniformly satisfied regardless of the level of personalization.

Can a collaborative healthcare network improve the care of people with epilepsy

New opportunities are now available to improve care in ways not possible previously. Information contained in electronic medical records can now be shared without identifying patients. With network collaboration, large numbers of medical records can be searched to identify patients most like the one whose complex medical situation challenges the physician. The clinical effectiveness of different treatment strategies can be assessed rapidly to help the clinician decide on the best treatment for this patient. Other capabilities from different components of the network can prompt the recognition of what is the best available option and encourage the sharing of information about programs and electronic tools. Difficulties related to privacy, harmonization, integration, and costs are expected, but these are currently being addressed successfully by groups of organizations led by those who recognize the benefits.

Diagnostic performance dashboards: Tracking diagnostic errors using big data

In their 2015 report, Improving Diagnosis in Healthxa0Care , the National Academy of Medicine asserted that most individuals ‘will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences’1 and ‘improving the diagnostic process is not only possible, but it also represents a moral, professional, and public health imperative.’1 A key barrier to eliminating diagnostic errors is the lack of operational measures to track diagnostic performance.2 3nnNovel approaches using ‘big data’ to identify statisticallyxa0meaningful patterns offer unique opportunities to operationalise measurement of diagnostic errors and misdiagnosis-related harms.4 In a study ofxa0~190u2009000 US inpatient stroke admissions, thexa0authors found missed opportunities to diagnose stroke early were often linked to clinical presentations with dizziness or vertigo.5 A graphical temporal profile analysis of treat-and-release emergency department (ED)xa0visits showed an exponential increase in visit frequency in the days before stroke admission, establishing these as likely misdiagnoses.5nnWe operationalised this approach by constructing a diagnostic performance dashboard to monitor diagnostic quality and safety. Kaiser Permanente-Mid Atlantic Permanente Medical Group (KP) and the Johns Hopkins University School of Medicine (JHM) partnered to build a learning ecosystem using visual analytics tools. Visual analytics combines expert knowledge with machine computational power for smart data exploration.6 Visual representations allow users to see the big picture and visually explore relevant data. Interactive data discovery supports ‘slice-and-dice’ operations with data drill-through capabilities, enabling exploratory data mining, hypothesis testing and decision making.nnLeveraging the exploratory data …

Data-driven approaches to augment clinical decision in EMR Era

Patient data in electronic medical record systems can serve as an incredible source of evidence on the real-world evaluation of the relationship between medicine treatments and the well-being of the patient. However, the vast amount of raw data presents a significant bottleneck for physician to make sense from it, thus limiting its use in a time pressured clinical setting. A niche exists to develop approaches to enhance a physicians ability to process large volume of data with the use of `external aids in an effort to augment their data cognition capabilities. In this paper, we present how a symbiotic relationship between analysis and visualization techniques can be used to help make sense of a large patient data set. Using examples, we demonstrate our data-driven approach, and show how it can serve a physician in the clinical decision support role at the point of care.

Predictive model of the treatment effect for patients with major depressive disorder

The model to evaluate and predict the effectiveness of treatment of the Major Depressive Disorder (MDD) was developed and estimated using MindLinc data. The clinical global impression (CGI) scale with seven categories was used to measure the patients state. The proportional odds model was selected because of ordinal nature of the outcome. The set of predictors included i) CGI score measured at preceded visit, ii) three groups of medications (antidepressants, atypical medicine, and augmentation medicine), all categorized for appropriate number of strata (from six to nine) and their daily doses, iii) psychiatric comorbidities, iv) type of the therapy used (talk vs. medications), v) demographic variables (e.g., age group, sex), and vi) the history of the efficiency of prior treatment. More than a half of a million records with measured CGI scores and their predictors were identified in the MindLinc database and used for model estimation. The predicted model of future CGI scales was developed and evaluated for single and recurrent episodes of MDD. Significant estimates were obtained for demographic factors, history of previous SGI scales, and for comorbidity and treatment indices. The methods of causal inferences based on the inverse probability weighting approach were applied to evaluate the treatment effects. The model extensions allowing for addressing the limitations of the proportional odds model are discussed.

Visual Analytics to Optimize Patient-Population Evidence Delivery for Personalized Care

Electronic medical records (EMR) can be used to identify cohorts of patients who are clinically comparable to an individual patient. In this paper, we describe an approach that applies visual analytics to EMR data to describe the clinical course for an individual patient, display outcomes for a comparable cohort stratified by treatment, and generate predictions regarding a patients clinical course based on treatment options. The visual display of information is designed to help clinicians choose among alternative therapies based on the EMR-derived outcomes of the cohort.

Mapping patient treatment profiles and electronic medical records to a clinical guideline for use in patient care

Clinical guidelines reflect expert determined protocols for use in patient diagnosis and treatment. But these guidelines remain largely under used due to multiple reasons, including a lack of patient specific information, and for lack of integration with the clinicians workflow. Here we implement a novel approach to show the use of a guideline as a reference map to present a visual overview of the patients treatment profile. We also demonstrate the use of the reference map approach to present electronic medical record data in the context of a comparative patient population. Further, we show the utility of this approach for integration within the clinicians workflow to facilitate their decision making process at the point of care.