M2SYS Healthcare Solutions Free Online Learning Podcasts

Podcast length – 45:13

Topic: “Big Data” in Healthcare: What it Means, How it Promises to Reshape Healthcare, Roadblocks to Extract

Meaningful Information, How Meaningful Use Enables Big Data, Cross-Country Collaboration, Effect on Population

Management, Impact on Privacy, & More!

Mandi Bishop, Principal, Adaptive Project Solutions

Topics Covered in Podcast:

Defining Big Data & Identifying the Main Drivers

How Does Big Data Promise to Reshape Healthcare?

Primary Roadblocks for Providers to Extract Meaningful Information from Big Data

How Meaningful Use Enables Big Data Techniques to be Used in Healthcare

The “Health Cloud” Initiative & More on Aggregated Big Data Platforms

Topics Covered in Podcast (continued):

Big Data’s Implications on Patient Privacy

Most Effective Security Technologies to Protect Patient Data Access

The Role of Biometric Identification Technology in Health Information Exchanges

• “Big Data” encompasses petabytes and petabytes of patient data information (clinical data, claims data, personal health record data, quantified self data)

• Several advances in technology have manifested the Big Data movement: • Computing power • Connectivity speed • Faster query times

• All of these advances have allowed healthcare to leverage big data analytics in a way that was not possible 10 years ago

• Big data will continue to become more applicable to healthcare as the move from paper to electronic health records continues

• Healthcare as an industry has hampered itself in its use of big data analytics by not standardizing data capture mechanisms until recently

• Data capture standardization is creating opportunities for downstream big data analytics across clinical and claims continuum

Defining Big Data and Identifying the Main Drivers

Defining Big Data and Identifying Main Drivers (continued)

• Up until the advent of the Health Insurance Portability & Accountability Act (HIPAA), healthcare facilities were responsible for maintaining data internally

• The expectation has shifted towards hospitals sharing data outside of their four walls with external entities creating larger and larger data sets

• Effective analytics must come from standard data formats and clinical data sets must speak the same vocabulary (semantic interoperability) and currently this does not exist

• Opportunity exists for big data to address the quality of patient care • Big data stands to make a significant impact on chronic disease treament • Fiscal incentives and financial aspects of big data analytics are what is

currently driving the healthcare industry until we figure out a way to tie a definition of quality to clinical outcome improvement

How Does Big Data Promise to Reshape Healthcare?

• Population management – moving away from a fee-for-service payment model and moving towards value based pricing and population cost management will provide an opportunity to identify trends – locating outliers and correlations in large data sets for example • When dealing with population as a whole, big data may provide ability

to identify a more effective treatment protocol than a more closely controlled trial size of population

• Accountable Care Organizations (ACOs) may also have impact on identifying previously unforeseen correlative relationships

• There is both opportunity and risk • Large data sets allow ACOs to manage member risk similar to insurance

companies • Big data enables the more rapid assessment of clinical trials • Population health management should begin to shift away from a focus on

cost and more towards a focus on health & improved quality of life

Population Health Management

How Does Big Data Promise to Reshape Healthcare? (continued)

• Personalized medicine – proteomics, genomics, quantified self movement has the ability to be drastically effected by big data – e.g. personalized gene therapy

• Information gleaned from big data in population management trickles down to individualized medicine

• Analyzing the care coordination across a provider network can be applied to personalized medicine

Personalized Medicine

Did you know? It doesn’t matter the size of your company, big data is an area must be carefully examined as you grow. According to a recent online poll, 76% of small businesses view big data as an opportunity for growth.

Primary Roadblocks for Providers to Extract Meaningful Information from Big Data

• #1 and most obvious reason is money – healthcare providers aren’t funded like insurance companies – it takes a significant information technology investment to enable effective analytics • Smaller healthcare providers may not have access to those kind of

funds • Smaller providers may not have the ability to invest in data analytics IT

infrastructure unless they join larger Integrated Delivery Networks (IDNs) to leverage cost sharing

• Paper process is still a significant barrier • A lack of universal standards for clinical data remains a barrier • Not all hospitals use HL7 clinical data capture language and some are still

communicating in proprietary formats • Incentives to stay proprietary because of patent issues, treatment

protocols are additional barriers

How Meaningful Use Enables Big Data Techniques to be Used in Healthcare

• There are a few components of Meaningful Use that are integral for effective big data analytics: • Data capture – Meaningful Use guidelines require that specific

clinical data elements be captured in a standard format • Interoperability – Meaningful Use standardizes the transport

mechanisms between electronic health record (EHR) systems – helps to amass a set of meaningful analytics and apply that to population management

• Patient engagement – patients will have the ability to get full medical records in the same format from disparate providers

The “Health Cloud” Initiative & More on Aggregated Big Data Platforms

• “Health Cloud” is a joint effort by U.S. (MedRed) and UK (BT Health) – it’s an open data repository of approximately 50 million people and 5 years of data that includes: • Clinical encounter • Pharmacy utilization • Outcomes • Desire to include CMS & FDA event reporting data sets

• Hope is that larger data set will help to proactively identify larger problems – (e.g. – the possibility of predicting the next FDA recall)

• Tremendous opportunity especially if contextual data is layered in down the road (e.g. – geographic, demographic, even weather data – anything that can potentially impact health)

• Expect to see more initiatives like the “Health Cloud” as the public demand rises and data capture tools improve

• Currently, “Health Cloud” efforts are primarily focused on pharmaceutical industry – expect to see shift in next couple of years

• Expect to see more cross country collaboration on big data in healthcare moving forward

• Other countries have already been leveraging the power of big data in healthcare longer and more effectively than the U.S. because the U.S. market has been so focused on privacy and data silos

• Clinical data standards that cross countries like HL7 – as we continue to develop multi-language and improved language capabilities, and natural language processing, collaborations will become very effective and provide us with global data sets – helps to look at the human population as a whole

The “Health Cloud” Initiative & More on Aggregated Big Data Platforms

Big Data’s Implication on Patient Privacy • Big data can be a frightening concept, especially for those with low

medical literacy • Patients are legitimately concerned that specific types of sensitive data

will be shared amongst entities without their permission (e.g. – some patients prefer that their mental health data not be shared with their primary care provider or lab results going to an insurance payor)

• Patients themselves limit the efficacy of the information listed on their own individual medical record through opt-outs

• Privacy in healthcare is a double edged sword: • Fines/penalties in place for data breaches are significant and there

is a much larger data set available now for patients who do opt-in and are victims of breach

• Health Information Exchanges (HIEs) have opened the door for larger breaches and exposure to sensitive information that is detrimental to patient privacy

• However, the sharing of data amongst providers is helping to contribute to better care coordination & clinical decision support

Most Effective Security Technologies to Protect Patient Data Access

• The shift from paper to electronic health records necessitates a shift change in how to effectively protect patient data

• Patient data information used to be limited and siloed – the advent of EHR’s, HIEs, Meaningful Use mandates, and an increased interest in leveraging the power of big data to perform population management has increased the availability of electronic information that is easier to transport (and steal)

• Critical that a security protocol be in established & observed to: • Validate a patient’s identity & ensure they are who they say they are

both in person and online (e.g. – patient portals) • Biometrics for patient identification is increasing and a viable tool to

verify a patient’s identity with near 100% accuracy – can also be used at each touch point along the continuum of care to authenticate identity before service/procedure is rendered

The Role of Biometric Identification Technology in Health Information Exchanges

• Tremendous opportunity for biometrics to play a critical role in uniquely identifying a patient

• Universal standards on the use of biometrics for identification are still evolving & could temporarily hamper the rapid pace of adoption

• Biometrics are proving to be the easiest and most definitive way to validate a patient’s identity

• The judicious application of biometrics that follows standards with an alternative in place for providers who can’t afford to leverage biometrics is critical for wider adoption

Did you know? Not all biometric identification solutions are the same. When researching which is best for you, make sure that no patient contact is needed to support hospital infection control and the back end matching is one-to-many.

Thank you to Mandi for her time and knowledge for this podcast!

Please follow Mandi on Twitter (@mandibpro) and visit her LinkedIn page

under: Adaptive Project Solutions

John Trader PR and Marketing Manager M2SYS Healthcare Solutions

1050 Crown Pointe Pkwy. Suite 850

Atlanta, GA 30338 jtrader@m2sys.com

770-821-1734 www.m2sys.com/healthcare

Podcast home page: http://www.m2sys.com/healthcare/healthcare-biometrics-podcasts/

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