Data is one of the most valuable assets in any organization and is necessary to sustain current and future business models. As healthcare transitions into a more analytically driven industry, managing data is especially relevant. Organizations are grappling with ways to manage continual changes in health information technology (IT), IT infrastructure, and the huge volume of data collected across the healthcare industry. The push toward value-based care has amplified the need for efficient exchange of quality patient data, which fills gaps in information and offers providers and payers a more complete picture of the patient. Data-centric strategies focused on managing the entire lifecycle of healthcare data are particularly important in today’s environment.

The policies and procedures to manage, protect, and govern information across a healthcare enterprise falls under data governance. Data governance includes data modeling, data mapping, data audit, data quality controls, data quality management, data architecture, and data dictionaries. A strong data governance structure is a critical component of any healthcare organization, as it provides a structure for analytics and other complex data initiatives.

In Spring 2018, eHealth Initiative Foundation and the LexisNexis® Risk Solutions healthcare business hosted the first in a series of roundtable meetings on data governance in healthcare. The meeting convened senior executives from stakeholder groups, including payer, provider, professional organizations, health information exchanges (HIEs), research, public health, laboratory, and pharmaceuticals. The goal of the meeting was to gather expert opinions on how to make data accessible, close quality gaps, turn insight into action, and protect sensitive patient information. This brief addresses the value of data governance in healthcare; existing challenges related to data governance; and key takeaways from the meeting.

Bridging the Digital Divide: Mobile Access to Personal Health Records Among Patients With Diabetes

Some patients lack regular computer access and experience a digital divide that causes them to miss internet-based health innovations. The diffusion of smartphones has increased internet access across the socioeconomic spectrum, and increasing the channels through which patients can access their personal health records (PHRs) could help bridge the divide in PHR use. We examined PHR use through a computer-based Web browser or mobile device. Mobile-ready PHRs may increase access among patients facing a digital divide in computer use, disproportionately reaching racial/ethnic minorities and lower SES patients. Nonetheless, even with a mobileoptimized and app-accessible PHR, differences in PHR use by race/ethnicity and SES remain. Continued efforts are needed to increase equitable access to PHRs among patients with chronic conditions.

The full article can be viewed below.  

Author: Ralph Johnson III

Federal and state mandates have compelled healthcare systems to adopt “meaningful use” electronic health record (EHR) systems. Off-the-shelf, onthe-spot, one-source EHR systems such as EPIC® have become popular choices. Indeed, EPIC® recently captured a substantial proportion of the Houston Texas Medical Center (TMC), CVS Pharmacy mini-clinics, and extended into academic institutions. Current reported estimates are contentious but vary between 2047% of the EHR market share. Therefore, it is only sensible to conduct a review of EPIC.  

Hospitals’ and health systems’ ongoing prioritization of health information technology (IT) tools continues to expand patients’ ability to engage with their providers, access their health data, and interact with the health care system electronically. It also allows providers to more readily communicate across settings of care, supporting greater care coordination. In a patient-centered, value-driven care model, the ability of patients to interact and engage with both their health data and the health care delivery system electronically is a key driver of high-quality health care. 

This is the first in a series of issue briefs highlighting data from the 2016 AHA Annual Survey Information Technology Supplement for community hospitals
collected November 2016 – April 2017.1 This brief focuses on the state of patient's access to and engagement with their health data through health IT. Results are grouped into three categories of activity: accessing health data, interacting with health data, and obtaining health care services.

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Presenters: 

Milisa K Rizer, MD, MPH, FAAFP, FHIMSS, CPHIMS

Chief Clinical Information Officer Professor of Family Medicine, Nursing, & Biomedical Informatics

The Ohio State University Wexner Medical Center
 

Thomas Bentley, RN, MS, FHIMSS, CPHIMS, CHCIO 

Deputy CIO

Objectives: 

• Identify the top three factors that improve user efficiency and satisfaction.
• Identify the top tools that can be used to improve the amount of time spent in documentation activities. 
• Identify the two areas of greatest frustration of users of EMRs.
• Identify one area where your staff can be used to help with provider efficiency.
• Identify one place where you can be involved with improving the EMR in your hospital or clinic. 

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Big Data Analytics Best Practices: Understanding its capabilities and potential benefits for healthcare organizations​

To date, the health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.

Best Practices

• Governance - Implementing big data governance successfully can allow for more efficient utilization of data
• Sharing - Developing an information sharing culture improves quality and accuracy 
• Training - Training key personnel to use big data analytics is the key to utilizing outputs effectively
• Cloud computing - Incorporating cloud computing into the organization's big data analytics can help address cost and data storage issues.  However, this must be balanced with patient information protection
• New ideas - Generating new business ideas from big data analytics promotes innovation, productivity, and competitiveness  

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.
 

Surescripts Advancing Interoperability Best Practices

The results are in and the data tells a clear story: interoperability is alive and well across American healthcare. In fact, Surescripts 2017 National Progress Report shows that interoperability is not only expanding but also transforming health data exchange.In particular, the Surescripts Network Alliance is effectively advancing healthcare by:

Best Practices

• Measuring accuracy - Perfecting e-prescribing by measuring accuracy—at scale—to improve patient safety and increase efficiency
• Price transparency - Empowering prescribers at the point of care with prescription price transparency
• Utilizing technology - Bringing technology to the front lines of the opioid epidemic
• Highlighting the big picture - Delivering true interoperability by giving healthcare professionals a more complete view of a patient’s medication and clinical history directly in their EHR workflow

Interoperability is Rapidly Advancing Across Healthcare

Digital Health Practices, Social Media Use, and Mental Well-Being Among Teens and Young Adults in the U.S.

This report presents the first set of descriptive findings from a nationally representative, probability-based survey of more than 1,300 U.S. teens and young adults, ages 14 to 22, conducted in February and March 2018. This initial report focuses on two main topics: first, young people’s self- described use of online health information and digital health tools, including those used for peer-to-peer health exchanges; and second, the associations between self-reported social media use and mental well-being among teens and young adults (TYAs).

15 minutes. That’s how long your doctor has to see you, assess your complaint, diagnose a solution and see you out the door – hopefully on the pathway back to wellness.
This isn’t much time, when you consider the wealth of information that he or she has to consider. Your patient record, the medical research relevant to your complaint, the answers about your condition that you provide, the basic examination (“say aaaaaaah”) that is carried out.

So how will your doctor cope when faced with the tsunami of healthcare information that will occur when it is routine for your patient record to include data about your genome, your microbiome (bugs in your body) and your fitness regime?

Your electronic health record is fast becoming the most powerful tool in the medical toolkit. All the information will be stored in the cloud. It will have to be because the size of the electronic file containing your complete patient record is estimated to be as much as six terabytes. That’s a quarter of the whole of Wikipedia (24Tbs)!

A data file that large is required to enable the practice of precision medicine. This a new revolution in healthcare. It is the ability to target healthcare treatment specifically for an individual.

In addition to improving health outcomes, precision medicine will save vital health dollars because it is enabled by unique data insights that lead to more targeted treatments.

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