An integrated big data analytics-enabled transformation model: Application to health care

A big data analytics-enabled transformation model based on practice-based view is developed, which reveals the causal relationships among big data analytics capabilities, IT-enabled transformation practices, benefit dimensions, and business values. This model was then tested in healthcare setting. By analyzing big data implementation cases, we sought to understand how big data analytics capabilities transform organizational practices, thereby generating potential benefits. In addition to conceptually defining four big data analytics capabilities, the model offers a strategic view of big data analytics. Three significant path-to-value chains were identified for healthcare organizations by applying the model, which provides practical insights for managers.

The full article can be viewed below.  

Implications of big data analytics in developing healthcare frameworks – A review

The domain of healthcare acquired its influence by the impact of big data since the data sources involved in the healthcare organizations are well-known for their volume, heterogeneous complexity and high dynamism. Though the role of big data analytical techniques, platforms, tools are realized among various domains, their impact on healthcare organization for implementing and delivering novel use-cases for potential healthcare applications shows promising research directions. In the context of big data, the success of healthcare applications solely depends on the underlying architecture and utilization of appropriate tools as evidenced in pioneering research attempts. Novel research works have been carried out for deriving application specific healthcare frameworks that offer diversified data analytical capabilities for handling sources of data ranging from electronic health records to medical images. In this paper, we have presented various analytical avenues that exist in the patient-centric healthcare system from the perspective of various stakeholders. We have also reviewed various big data frameworks with respect to underlying data sources, analytical capability and application areas. In addition, the implication of big data tools in developing healthcare eco system is also presented.

The full article can be viewed below.

Scalable and accurate deep learning with electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart.

The full article can be viewed below.
 

As Yuze Yang, PharmD and Pharmacist Data Manager at Surescripts explains, “Pharmacists are many things—counselors, medication experts and key members of a patient’s care team. And our ability to deliver safe and high quality care largely depends on the quality and accuracy of the e-prescriptions we receive.

A byzantine system of third-party payers, including insurers and government programs, keep consumers from understanding the true cost of their medical services, says Eli Lilly’s CEO.

PULSE POLL: Physician attention

Each month, the Truven Health Analytics PULSE® Healthcare Survey polls approximately 3,000 Americans to gauge attitudes and opinions on a wide range of healthcare issues. This independent, multi-modal (land line, cell phone, Internet) survey collects information from 80,000 randomly selected US households. The results depicted here represent responses from 3,003 survey participants interviewed from Nov. 3 – 15, 2017, and 3,007 participants interviewed from Oct. 1 – 13, 2013. The margin of error in both polls is +/- 1.8 percentage points. Truven Health is part of the IBM Watson Health™ business.This Truven Health Analytics PULSE® Healthcare Survey asked Americans about their experiences with primary care physicians. Respondents were asked identical questions to a study conducted in 2013, providing a comparison. Some of the topics covered include scheduling, device usagee, and time spent with patient. 

The full results can be viewed below.  

HEALTH POLL: Prescription Drugs

Every other month, the Truven Health Analytics®-NPR Health Poll surveys approximately 3,000 Americans to gauge attitudes and opinions on a wide range of healthcare issues. Poll results are reported by NPR on the health blog Shots (npr.org/sections/health-shots/) and on air. The Truven Health Analytics-NPR Health Poll is powered by the Truven Health Analytics PULSE® Healthcare Survey, an independently funded multi-modal (land line, cell phone, internet) survey that collects information from approximately 80,000 US households annually. The results depicted here represent responses from 3,003 survey participants interviewed from June 1 – 15, 2017. The margin of error is +/-1.8 percentage points

Given the amount of money spent on retail prescription drugs in the US ($324.6 billion in 2015 ), the Truven Health Analytics-NPR Health Poll asked Americans about their experiences with and attitudes toward prescription drugs and drug pricing. While 97% of respondents who received a prescription for medication in the last 90 days filled it, the most cited reason by respondents who did not fill their prescription was cost (67%), and 12% of all respondents said that cost drove them to purchase prescription medication outside the US. 

The full results can be viewed below.  

Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative

Selection of systemic therapy for primary breast cancer is currently based on clinical biomarkers along with stage. Novel genomic tests are continuously being introduced as more precise tools for guidance of therapy, although they are often developed for specific patient subgroups. The Sweden Cancerome Analysis Network – Breast (SCAN‐B) initiative aims to include all patients with breast cancer for tumour genomic analysis, and to deliver molecular subtype and mutational data back to the treating physician. An infrastructure for collection of blood and fresh tumour tissue from all patients newly diagnosed with breast cancer was set up in 2010, initially including seven hospitals within the southern Sweden regional catchment area, which has 1.8 million inhabitants. Inclusion of patients was implemented into routine clinical care, with collection of tumour tissue at local pathology departments for transport to the central laboratory, where routines for rapid sample processing, RNA sequencing and biomarker reporting were developed. More than 10 000 patients from nine hospitals have currently consented to inclusion in SCAN‐B with high (90 per cent) inclusion rates from both university and secondary hospitals. Tumour samples and successful RNA sequencing are being obtained from more than 70 per cent of patients, showing excellent representation compared with the national quality registry as a truly population‐based cohort. Molecular biomarker reports can be delivered to multidisciplinary conferences within 1 week. Population‐based collection of fresh tumour tissue is feasible given a decisive joint effort between academia and collaborative healthcare groups, and with governmental support. An infrastructure for genomic analysis and prompt data output paves the way for novel systemic therapy for patients from all hospitals, irrespective of size and location.

The full article can be viewed below.

Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults

Reducing premature mortality associated with age-related chronicdiseases, such as cancer and cardiovascular disease, is an urgent priority. We report early results using genomics in combination with advanced imaging and other clinical testing to proactively screen for age-related chronic disease risk among adults. We enrolled active, symptom-free adults in a study of screening for age-related chronic diseases associated with premature mortality. In addition to personal and family medical history and other clinical testing, we obtained whole-genome sequencing (WGS), noncontrast whole-body MRI, dualenergy X-ray absorptiometry (DXA), global metabolomics, a new blood test for prediabetes (Quantose IR), echocardiography (ECHO), ECG, and cardiac rhythm monitoring to identify age-related chronic disease risks. Precision medicine screening using WGS and advanced imaging along with other testing among active, symptom-free adults identified a broad set of complementary age-related chronic disease risks associated with premature mortality and strengthened WGS variant interpretation. This and other similarly designed screening approaches anchored by WGS and advanced imaging may have the potential to extend healthy life among active adults through improved prevention and early detection of age-related chronic diseases (and their risk factors) associated with premature mortality.

The full article can be viewed below.  

Social Support is Associated with Medication Adherence

Functional social support has a stronger association with medical treatment adherence than structural social support in several populations and disease conditions. Using a contemporary U.S. population of adults treated with medications for coronary heart disease (CHD) risk factors, the association between social support and medication adherence was examined.  Seeing multiple friends and relatives was associated with better medication adherence among individuals with CHD risk factors. Thus increasing social support with combined structural and functional components may help support medication adherence.

The full article, entitled "Association of functional and structural social support with medication adherence among individuals treated for coronary heart disease risk factors: Findings from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study" can be viewed below.