Browsing by Author "Kasthurirathne, Suranga"

Now showing 1 - 6 of 6
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    Evaluating the effect of data standardization and validation on patient matching accuracy
    (Oxford, 2019-05) Grannis, Shaun; Xu, Huiping; Vest, Josh; Kasthurirathne, Suranga; Bo, Na; Moscovitch, Ben; Torkzadeh, Rita; Rising, Josh; Family Medicine, School of Medicine
    Objective This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. Materials and Methods We used 4 manually reviewed datasets, containing a random selection of matches and nonmatches. Matching datasets included health information exchange (HIE) records, public health registry records, Social Security Death Master File records, and newborn screening records. Standardized fields including last name, telephone number, social security number, date of birth, and address. Matching performance was evaluated using 4 metrics: sensitivity, specificity, positive predictive value, and accuracy. Results Standardizing address was independently associated with improved matching sensitivities for both the public health and HIE datasets of approximately 0.6% and 4.5%. Overall accuracy was unchanged for both datasets due to reduced match specificity. We observed no similar impact for address standardization in the death master file dataset. Standardizing last name yielded improved matching sensitivity of 0.6% for the HIE dataset, while overall accuracy remained the same due to a decrease in match specificity. We noted no similar impact for other datasets. Standardizing other individual fields (telephone, date of birth, or social security number) showed no matching improvements. As standardizing address and last name improved matching sensitivity, we examined the combined effect of address and last name standardization, which showed that standardization improved sensitivity from 81.3% to 91.6% for the HIE dataset. Conclusions Data standardization can improve match rates, thus ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety.
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    Evaluating Two Approaches for Parameterizing the Fellegi-Sunter Patient Matching Algorithm to Optimize Accuracy
    (Medinfo conference proceedings, 2019-08-25) Grannis, Shaun; Kasthurirathne, Suranga; Bo, Na; Huiping, Xu
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    Generalization of Machine Learning Approaches to Identify Notifiable Diseases Reported from a Statewide Health Information Exchange
    (MEDINFO Conference proceedings, 2019-08-25) Dexter, Gregory; Kasthurirathne, Suranga; Dixon, Brian E.; Grannis, Shaun
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    How Good Are Provider Annotations?: A Machine Learning Approach
    (Wiley, 2017-01) Malas, M. Said; Kasthurirathne, Suranga; Moe, Sharon; Duke, Jon; Department of Medicine, IU School of Medicine
    Introduction: CMS-2728 form (Medical Evidence Report) assesses 23 comorbidities chosen to reflect poor outcomes and increased mortality risk. Previous studies questioned the validity of physician reporting on forms CMS-2728. We hypothesize that reporting of comorbidities by computer algorithms identifies more comorbidities than physician completion, and, therefore, is more reflective of underlying disease burden. Methods: We collected data from CMS-2728 forms for all 296 patients who had incident ESRD diagnosis and received chronic dialysis from 2005 through 2014 at Indiana University outpatient dialysis centers. We analyzed patients' data from electronic medical records systems that collated information from multiple health care sources. Previously utilized algorithms or natural language processing was used to extract data on 10 comorbidities for a period of up to 10 years prior to ESRD incidence. These algorithms incorporate billing codes, prescriptions, and other relevant elements. We compared the presence or unchecked status of these comorbidities on the forms to the presence or absence according to the algorithms. Findings: Computer algorithms had higher reporting of comorbidities compared to forms completion by physicians. This remained true when decreasing data span to one year and using only a single health center source. The algorithms determination was well accepted by a physician panel. Importantly, algorithms use significantly increased the expected deaths and lowered the standardized mortality ratios. Discussion: Using computer algorithms showed superior identification of comorbidities for form CMS-2728 and altered standardized mortality ratios. Adapting similar algorithms in available EMR systems may offer more thorough evaluation of comorbidities and improve quality reporting.
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    An Incremental Adoption Pathway for Developing Precision Medicine Based Healthcare Infrastructure for Underserved Settings
    (Medinfo 2017 Conference proceedings, 2017-08) Kasthurirathne, Suranga; Biondich, Paul; Mamlin, Burke; Cullen, Theresa; Grannis, Shaun
    Recent focus on Precision medicine (PM) has led to a flurry of research activities across the developed world. understaffed and underfunded health care systems in the US and elsewhere evolve to adapt PM to address pressing But how can healthcare needs? We offer guidance on a wide range of sources of healthcare data / knowledge sources as well as other infrastructure / tools that could inform PM initiatives, and may serve as low hanging fruit easily adapted on the incremental pathway towards a PM based healthcare system. Using these resources and tools, we propose an incremental adoption pathway to inform implementers working in underserved communities around the world on how they should position themselves to gradually embrace the concepts of PM with minimal interruption to existing care delivery.
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    The Role of Social Workers in Addressing Patients' Unmet Social Needs in the Primary Care Setting
    (2021-04) Bako, Abdulaziz Tijjani; Vest, Joshua R.; Blackburn, Justin; Walter-McCabe, Heather; Kasthurirathne, Suranga; Menachemi, Nir
    Unmet social needs pose significant risk to both patients and healthcare organizations by increasing morbidity, mortality, utilization, and costs. Health care delivery organizations are increasingly employing social workers to address social needs, given the growing number of policies mandating them to identify and address their patients’ social needs. However, social workers largely document their activities using unstructured or semi-structured textual descriptions, which may not provide information that is useful for modeling, decision-making, and evaluation. Therefore, without the ability to convert these social work documentations into usable information, the utility of these textual descriptions may be limited. While manual reviews are costly, time-consuming, and require technical skills, text mining algorithms such as natural language processing (NLP) and machine learning (ML) offer cheap and scalable solutions to extracting meaningful information from large text data. Moreover, the ability to extract information on social needs and social work interventions from free-text data within electronic health records (EHR) offers the opportunity to comprehensively evaluate the outcomes specific social work interventions. However, the use of text mining tools to convert these text data into usable information has not been well explored. Furthermore, only few studies sought to comprehensively investigate the outcomes of specific social work interventions in a safety-net population. To investigate the role of social workers in addressing patients’ social needs, this dissertation: 1) utilizes NLP, to extract and categorize the social needs that lead to referral to social workers, and market basket analysis (MBA), to investigate the co-occurrence of these social needs; 2) applies NLP, ML, and deep learning techniques to extract and categorize the interventions instituted by social workers to address patients’ social needs; and 3) measures the effects of receiving a specific social work intervention type on healthcare utilization outcomes.