In this interview, Anuj Desai – Vice President of Market Development at New York eHealth Collaborative talks about his participation in the upcoming panel, “Freeing the Data: Driving Value from Using Clinical, Claims, and Device Data” at this year’s Digital Health Conference breakout session on 11/14.
Anuj identifies some of big data’s challenges and opportunities and provides his insight on best practices for healthcare organizations to optimize their data to get an accurate picture of a patient’s medical record.
Can you provide a brief background/overview of yourself and New York eHealth Collaborative?
The New York eHealth Collaborative (NYeC) is a not-for-profit organization, working to improve healthcare for all New Yorkers through health information technology (health IT). Founded in 2006 by healthcare leaders, in partnership with the New York State Department of Health, NYeC receives funding from state and federal grants to serve as the focal point for health IT in the State of New York.
NYeC works to develop policies and standards, to assist healthcare providers in making the shift to electronic health records and to coordinate the creation of a network to connect healthcare providers statewide – the Statewide Health Information Network of New York (SHIN-NY). The goal of NYeC is that no patient, wherever they may need treatment within the State of New York, is ever without fast, secure, accurate and accessible information.
I am the Vice President of Market Development at NYeC. I am responsible for our market facing activities including creation of the an API layer on top of the SHIN-NY that provides access to clinical data to tech companies building innovation applications; the New York Digital Health Accelerator that pairs startups with large healthcare systems; and the EHR/HIE Interoperability Workgroup, a collaborative of 19 states and 46 health IT vendors that have come together to drive interoperability.
You will be speaking at the upcoming Digital Health Conference break out session panel on, “Freeing the Data: Driving Value from Using Clinical, Claims and Device Data.” What can attendees of this breakout session learn about the future of big data in healthcare?
This session focuses on the strategies that different organizations are using to open up access to health data and unleash its inherent value, so as to create an ecosystem of data-enabled applications. As we have seen in other industries, such as digital media and tech, there is much value to be gained from opening up access to data. For example, when Google Maps opened access to its data, it spawned an entire community of applications leveraging this data.
In this panel, we will hear from the New York State Department of Health that recently was awarded the first Data Liberator Award from Health and Human Services for opening access to its data. Then, we will hear from large companies like athenahealth that is developing an EHR data platform integrated with apps, and Qualcomm that is focused on deriving value from device data.
I will discuss the SHIN-NY API and how we are providing access to clinical data to enhance provider software solutions. Deborah Estrin will talk about Open mHealth, and last, we will hear from Ginger.IO’s Julia Bernstien who will describe how patient reported data can be used in a novel way.
We can expect the panelists to offer predictions regarding the future trends in this market and describe how entrepreneurs and developers can build better products using the new types of data that is becoming available.
Why do you think there has been such an increase focus on driving more value from data in healthcare recently?
The healthcare industry is beginning to recognize that the days of keeping health data sequestered in proprietary silos have passed into oblivion. Consumers are eager to gain access to their health histories and to share them with their providers of care. Payers, employers, providers and government agencies have come to realize that reforming healthcare is, in large measure, dependent on sharing important clinical data.
The shift towards paying for health services based on outcomes rather than on procedures has focused attention on analytic techniques that can accurately distinguish between good and poor results. Finally, the unprecedented proliferation of smartphones and mobile devices has, for the first time, made it feasible to consider the collection and analysis of vast quantities of relevant data that can yield valuable insights into how to improve healthcare across the nation.
With 80% of medical data believed to be unstructured, what are the some of the challenges/barriers in taking this data and making it more structured and meaningful?
Health Information Exchanges (HIEs). This lack of interoperability has caused a great deal of healthcare data, that is generated by providers through referrals and transitions of care, to enter other systems in an unstructured format (primarily scans and faxes). Every time a provider sends a patient to another provider of place of service, if interoperability isn’t present, that data is sent in a flat format that cannot be “consumed” by the receiving system.
There are a few reasons why this is the case, primarily the cost of interfaces between systems is exorbitant, the interfaces themselves are complex often requiring a full-time engineer on each end of the interface and there are a variety of options for how these systems can “talk” to each other – lacking uniformity.
New York and 18 other states have joined forces with 47 EHR and HIE vendors to address the barriers to EHR-HIE interoperability. This consortium, the EHR\HIE Interoperability Workgroup, has ratified interoperability standards rooted in other industry specifications to enable “plug and play” interoperability between EHRs and HIEs. The adoption of these specifications will drive true interoperability and exchange of discrete healthcare data between end points (e.g. provider offices, hospitals and long term care facilities) without great expense and development time.
What are some best practices for healthcare organizations to optimize their data to get an accurate picture of a patient’s medical record?
From a quality of care perspective, it has always been important for healthcare organizations to have a full picture of what is happening in their patient’s health via their medical records. Now that healthcare delivery mechanisms and payment models are changing (e.g. ACOs), having this visibility to a complete picture of a patient’s records is even more important. Continuity of Care Documents (CCD) are an integral tool when it comes to caring for patients who receive care in various settings.
A best practice for healthcare organizations is to use an EHR system that can both generate and consume a CCD in a consistent manner with discrete data elements ideally in conjunction with an HIE which has access to community-level data for a patient. Additionally, it is important for healthcare organizations to capture device data (blood pressure, spirometry) as discrete data via interoperable devices.
Predictive analytics seems to be the next frontier in truly achieving value based healthcare, what innovative tools and technologies are you seeing out there?
Predictive analytics hold great promise in assessing the efficacy of healthcare. A top of mind example is predicting hospital readmissions so as to effect early interventions with patients whose conditions and behavior are associated with a high degree of recidivism. The most valuable tools are likely to be those which enable management of huge data stores and effective data mining of large quantities of data in an ad hoc fashion.
Tools are emerging that avoid the large investments entailed by classical data warehousing and analysis solutions historically marketed by large HIT vendors. The adoption of electronic health record systems across the nation provides a solid basis for innovative analytics solutions to peel back new layers of insight that expose ever more value in healthcare.