Weekly Roundup for Big Data in Medical Science: April 21-28, 2014

 

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Data graphic created from the Institute for Health Metrics and Evaluation web app showing the number of years people with chronic kidney disease live with their disability after diagnosis.

Data graphic created from the Institute for Health Metrics and Evaluation web app showing the number of years people with chronic kidney disease live with their disability after diagnosis.

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This week,  IBM Launches Watson-based big data services for clinical carePersephone, the Real-Time Genome Browser, and yet another online flu web-page view correlation…Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real time

 

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Upcoming Events

Link When What Where
MIWC 2014 April 28-29, 2014 Medical Informatics World Conference Boston, MA
BDM 2014 May 21-23 2014  Big Data in Biomedicine Conference Stanford, CA
ASE BDS 2014 May 27-31, 2014  Second ASE International Conference on Big Data Science and Computing Stanford, CA
HCI-KDD@AMT 2014 August 11, 2014  Special Session on Advanced Methods in Interactive Data Mining for Personalized Medicine Warsaw, Poland
BigR&I 2014 August 27-29, 2014  International Symposium on Big Data Research and Innovation Barcelona, Spain
ICHI 2014 September 15-17, 2014  IEEE International Conference on Healthcare Informatics Verona, Italy

The Big Medicare Payment Data Release

Today Medicare released payment data for over 880,000 healthcare providers, and include charge and payment information, provider specialties and addresses, billing codes, and other specific information.  The Medicare data set is downloadable here.  The description on the Medicare web site describes the data set as:

“Provider Utilization and Payment Data: Physician and Other Supplier Public Use File (Physician and Other Supplier PUF), with information on services and procedures provided to Medicare beneficiaries by physicians and other healthcare professionals.  The Physician and Other Supplier PUF contains information on utilization, payment (allowed amount and Medicare payment), and submitted charges organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code, and place of service. This PUF is based on information from CMS’s National Claims History Standard Analytic Files. The data in the Physician and Other Supplier PUF covers calendar year 2012 and contains 100% final-action physician/supplier Part B non-institutional line items for the Medicare fee-for-service population.”

There are some notable caveats to making conclusions about the data, which have been extensively outlined by docgraph.org.  Problems such as payer mix and specialty bias should be considered.  For example, pediatricians will have many fewer Medicare patients, while specialties with patients 65+ or special Medicare programs, such as Nephrology (Disclosure:  this my sub-specialty), may have a higher proportion of Medicare insured patients.

How will this large data set help us understand healthcare practices in the United States?  Several promising analyses come to mind:

  • Analysis of varying payment amounts for similar procedures – Because the same medical procedure can be billed on several different codes that account for the complexity of care provided, there is the opportunity for the “Lake Woebegone Effect” – where all the procedures have above average difficulty.  In some cases it might be true that a particular physician specializes in the most difficult cases (e.g. advanced chemotherapy using an implantable pump for liver cancer), but this is the exception rather than the rule.

 

  • Network analysis of unusual billing patterns -Here is where coupling this database with DocGraph (see my previous post here), a network graph database of all the referral patterns for Medicare for all US patients, may yield very interesting findings.  Some networks of physicians may have unusual billing patterns compared with others.  In some cases, this will be a sign of efficiency and great medical care delivery.  In others, it may be a sign of inefficiency or, in rare cases, something more ominous such as a pattern of fraud among a group or organization of providers.

 

  • Network analysis of procedure frequency – More useful, will be the ability to study types of procedures and visits among providers in different geographic areas, and the reimbursement variations.  Already, USA Today has posted a map of average reimbursement by state.  While some sophisticated analysis will be needed to reach thoughtful conclusions about regional variations in care, this will certainly spur a great deal of analysis and hopefully some good healthcare policy.

 

So, a good day for data transparency in healthcare delivery, and I say that as somebody whose Medicare practice is in the database! Let’s hope that high quality data analytics with thoughtful research follows.

 

Weekly Roundup: March 28 – April 5, 2014

 

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This week, a mesoscale-connectome of the mouse brainMerk uses Hadoop to optimize vaccine production, hospitals turn to big data to reduce re-admission rates, another philanthropic gift for data science.

 

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Upcoming Events

Link When What Where
MIWC 2014 April 28-29, 2014 Medical Informatics World Conference Boston, MA
BDM 2014 May 21-23 2014  Big Data in Biomedicine Conference Stanford, CA
ASE BDS 2014 May 27-31, 2014  Second ASE International Conference on Big Data Science and Computing Stanford, CA
HCI-KDD@AMT 2014 August 11, 2014  Special Session on Advanced Methods in Interactive Data Mining for Personalized Medicine Warsaw, Poland
BigR&I 2014 August 27-29, 2014  International Symposium on Big Data Research and Innovation Barcelona, Spain
ICHI 2014 September 15-17, 2014  IEEE International Conference on Healthcare Informatics Verona, Italy

March 21, 2014: Weekly Roundup for Big Data in Medical Science

 

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This week, Apple is rumored to enter the healthcare market, medical conspiracy theories without any data, Google wants your DNA sequences in the cloud for scientific discovery but didn’t get the flu predictions right, CMS proposes releasing more Medicare Part D data for research, and 2014: year of the wearable device?

 

Bits and Bytes

 

Upcoming Events

Link When What Where
MIWC 2014 April 28-29, 2014 Medical Informatics World Conference Boston, MA
BDM 2014 May 21-23 2014  Big Data in Biomedicine Conference Stanford, CA
ASE BDS 2014 May 27-31, 2014  Second ASE International Conference on Big Data Science and Computing Stanford, CA
HCI-KDD@AMT 2014 August 11, 2014  Special Session on Advanced Methods in Interactive Data Mining for Personalized Medicine Warsaw, Poland
BigR&I 2014 August 27-29, 2014  International Symposium on Big Data Research and Innovation Barcelona, Spain
ICHI 2014 September 15-17, 2014  IEEE International Conference on Healthcare Informatics Verona, Italy

8 March 2014: Weekly Roundup for Big Data in Medicine and Science

 

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HIVmap_gr2

Young S, Rivers C, Lewis B (2014) Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes.  Peventive Medicine.  http://dx.doi.org/10.1016/j.ypmed.2014.01.024

 

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This week, big data breaches at LA County medical facilities, more US healthcare delivery companies explore use of data mining and analytics.  At the Healthcare Information Management Systems Society meeting this week, “… all healthcare data is big data, and it’s only going to be getting bigger”.

 

Bits and Bytes

 

Upcoming Events

Link When What Where
BDM 2014 May 21-23 2014  Big Data in Biomedicine Conference Stanford, CA
MIWC 2014 April 28-29, 2014 Medical Informatics World Conference Boston, MA
ASE BDS 2014 May 27-31, 2014  Second ASE International Conference on Big Data Science and Computing Stanford, CA
HCI-KDD@AMT 2014 August 11, 2014  Special Session on Advanced Methods in Interactive Data Mining for Personalized Medicine Warsaw, Poland
BigR&I 2014 August 27-29, 2014  International Symposium on Big Data Research and Innovation Barcelona, Spain
ICHI 2014 September 15-17, 2014  IEEE International Conference on Healthcare Informatics Verona, Italy

Heathcare Data Privacy and Self-Insured Employers

Merge Data

In the rush to control healthcare costs, many employers are self-insuring.  As part of this move, most self-insured networks have become intensely interested in analyzing their own claims and medication cost data.  This type of analysis can be highly informative.  For example, Fred Trotter has created an enormous Medicare referral network graph (DocGraph) for all physicians and providers in the United States.  Essentially, he took Medicare claims data and counted the number of instances that two physicians billed for care on the same patients.  Physicians were identified by a unique National Practitioner Identifier (NPI) number, which is publicly available here.   By some very simple matrix manipulation on this very large data set of 2011 Medicare claims, he created DocGraph. The resulting data is very simple:  {provider #1, provider #2, number instances where P#1 billed for seeing patients that p#2 also saw at some point}, but very large (49 million relationships).  This graph can be used to identify referral “cliques” (who refers to whom), and other patterns.  The bottom line is that any organization that has claims data, big data storage and processing capabilities, and some very simple analytics can do this.  Similar analyses can be done for medication prescribing patterns, disability claim numbers, and other care-delivery metrics.

Now, this can be a good thing from a business standpoint.  For example, to contain costs, you want most of your patients treated by providers in your network where you have negotiated contracts.  Out-of-network treatments are termed “leakage” by the industry. Network “leakage” analysis can rapidly identify which physicians are referring out-of-network and how often.   Assuming that the equivalent services are available in-network, and this is the key question, you could make these physicians aware of the resources and craft a referral process that makes it easier for them and their patients to access care.

You can also identify physicians who are the “hubs” of your network,  practitioners who are widely connected to others by patient care. These may be the movers-and-shakers of care standards, and the group that you  want to involve in development of new patient care strategies.  For a great example, see this innovative social network analysis of physicians in Italy and their attitudes towards evidence based medicine.

These types of analyses are not without problems and could be used unwisely.  For example, physicians who prescribe expensive, non-generic medications may be highly informed specialists.  Programs that do not take such information into account may unfairly penalize network providers.  In addition, some services may not be available in-network, so providers referring out of network in these cases are actually providing the best care for their patients.  Finally, these analytics could easily be used to identify “high utilizers” of healthcare services, and to better manage their healthcare.  Network analytics are really good at such pattern recognition.  As we move forward, a balanced approach to such analytics is needed, especially to prevent premature conclusions from being drawn from the data.

There is a larger issue also lurking beneath the surface:  employee discrimination based on healthcare data.  Some healthcare networks are triple agents:  healthcare provider, employer, and insurer.  It may be tempting from a business side to use complex analytics to hire or promote employees based on a combined analysis of performance, healthcare and other data.  Google already uses such “people analytics” for hiring.  Some businesses may try to use such profiling, including internal healthcare claims data, to shape their workforce.  Even if individual health data is not used by a company, it seems likely that businesses will use de-identified healthcare data to develop HR  management systems.  See Don Peck’s article in the Atlantic for some interesting reading on “people management” systems.

As a last thought, it’s a bit ironic that we, as a healthcare system in the United States, will be spending hundreds of millions of dollars analyzing whether our patients going “out-of-network” for care, and designing strategies to keep them in network, when this problem does not exist for single-payer National Healthcare Systems…

Primary Care Genomics: The Next Clinical Wave?

DNA Double_HelixIs the main barrier for in healthcare analyzing and connecting the massive amounts of data present in electronic medical records, or is it generating the right data at the right level?  To really move healthcare forward, argues Michael Groner, VP of engineering and chief architect, and Trevor Heritage, we need to move research-level testing (whole exome sequencing, genomics, clinical proteomics) outside of the research environment and make it widely available to primary care physicians.  According to Groner, only when we amass large collections of such data will the true value of big data analytics methods be realized in medicine.

“It’s untenable to expect every physician or health care provider interested in improving patient care through the use of genomics testing to make the costly capital and other investments required to make this science a practical reality that impacts day-to-day patient care. Instead, the aim should be to connect the siloed capabilities associated with genomics testing into a simple, physician-friendly workflow that makes the best services accessible to every provider, regardless of geography or institutional size or affiliation…The true barrier to clinical adoption of genomic medicine isn’t data volume or scale, but how to empower physicians from a logistical and clinical genomics knowledge standpoint, while proving the fundamental efficacy of genomics medicine in terms of improved patient diagnosis, treatment regimens, outcomes and improved patient management.”

It’s a great dream, and parts of it will be realized in the future, but ignores many of the realities of in-the-trenches medical practice and medical science.  Genomics medicine will simply not improve the diagnostic acumen for many clinical problems; it’s just the wrong method.  Some examples include fractures, appendicitis, stroke, heart attacks, and many others.  Sequencing my genome will not diagnose my diverticulitis.  This has nothing to do with making genomic science and whole genome analytics a practical reality, but rather matching the tools to the appropriate medical problem and scale.  Genomics is quite good at providing information about genetic risk of conditions, but not necessarily diagnosing them.  Knowing that somebody has the BRCA1 breast cancer gene mutation does not tell you if they actually have breast cancer, and if they do which breast it’s in, whether it has metastasized, and where.

Groner’s larger point about the need to use data science to make personalized medicine a real-time reality, however, is well taken.  For example, the new guidelines for treatment of cholesterol abnormalities with statins, powerful cholesterol lowering drugs, are based on a risk score that no provider can calculate in their head.  Personalized medicine could evolve to generate a personalized risk assessment, based on a risk score for cardiovascular disease.  Beyond this, one could imagine the risk score being modified by a proteomics analysis of subtle serum proteins and their associated contributions to cardiovascular risk, and a genomic analysis of hereditary risk.  Integrating this evidence and providing clinicians with some measure of how to weight the predicted risk factors when making treatment decisions, are true growth areas for medical genomics and health informatics.