Friday, September 12, 2014

Understanding 'Open' Terminology

Having heard so many people using the terms “open systems”, “open computing”, and “open source” interchangeably, believing they all mean the same thing, it seemed appropriate to  write a short blog defining some of these terms and soliciting input on other ‘open’ terminology.

In general, the term “Open” often refers to initiatives whose inner workings are exposed to the public and are capable of being further modified or improved by any qualified individual or organization. “Open” is the opposite of “proprietary” or “closed” environments. In the case of software, this would mean that the “source code” is either open for all to access such as the Linux operating system or closed systems such as Windows  where only Microsoft programmers are able to change the source code. 

Other ‘open’ terminology often loosely bandied about include:
  • Open Source Software (OSS) - OSS refers to a software program in which the source code is available to anyone for use. It can be modified by anyone from its original design free of up-front license fees. The source code is available for review, modification, and sharing by the at-large community.
  • Open Standards - The set of specifications developed to define interoperability between diverse systems. The standards are owned and maintained by a vendor-neutral organization rather than by a specific commercial developer.
  • Open Systems - Hardware and/or software systems that use or adhere to open architecture and standards that support interoperable to some degree. See http://en.wikipedia.org/wiki/Open_systems
  • Open Architecture - An Information Technology (IT) architecture whose specifications are open and available to the public and that provide a platform that enables continued evolution and interoperability. See http://en.wikipedia.org/wiki/Open_architecture
  • Open Access - Providing free and unrestricted access to journal articles, research findings, books, and other literature. See http://www.soros.org/openaccess
  • Open Data – Data that anyone is free to use, reuse and redistribute without restriction. For more detail, see http://opendefinition.org.
  • Open Data Format - A standard way for describing data formats, per the “Open Data Format Initiative (ODFI)”, and a program to validate that a data file is “ODFI compliant”. See http://en.wikipedia.org/wiki/OpenDocument
  • Open Community - An environment in which the creative energy of large numbers of people is loosely coordinated into large, meaningful collaborative projects and generally avoids the traditional closed organization structure many are used to seeing in the private sector.
  • Open Computing - This is a general term used to describe an “open” philosophy in building information technology (IT) systems. It represents the principle that includes architecture and technology procurement policies and practices that align IT with the goals of an open interoperable computer systems environment.
  • Open Knowledge - An open system of knowledge transfer using the Internet and other information technologies to share best practices, emerging practices, knowledge and innovations within one or more “Community of Practice (CoP)” or across organizational boundaries. Visit http://okfn.org
  • Open Publication License (OPL) - This is a license used for creating free and open publications created by the Open Content Project. Other alternatives include the Creative Commons licenses, the GNU Free Documentation License and the Free Art License. See http://opencontent.org/openpub/
  • Open Source Hardware - Hardware whose design is made publicly available so that anyone can study, modify, distribute, make, and sell the hardware based on that ‘open’ design. See http://freedomdefined.org/OSHW
We are now seeing the emergence of new, related terms like ‘Open Culture’ and ‘Open Society’ as more people and organizations around the world adopt ‘open’ technologies and solutions and embrace the philosophy behind them.

Have you heard some other ‘open’ terminology being used that you can take a shot at defining and share with us?

Wednesday, September 3, 2014

UiEHR Presentation at OSEHRA Summit 2014


The Unified integrated Electronic Health Record (UiEHR) is a fully automated 'open architecture' solution initially designed for potential use by the Military Health System (MHS) and the Veterans Health Administration (VHA). It provides seamless real-time interoperability and data interchange between the EHR systems used by these two organizations.

TSRI spent the better part of ten years building Mumps to Java automatic conversion capabilities that now support a Unified integrated EHR (UiEHR) in the Cloud. After completing a pilot project demonstrating the capabilities of the system, TSRI has begun the rollout of a pre-alpha release of UiEHR in time for the OSEHRA 2014 Summit being held this week in Bethesda, Maryland. It is hoped that the OSEHRA community will review the system and participate in continuing efforts to improve the system over time.

Clearly no such universal iEHR/EMR system exists today. If it did, it would be omnipresent in the world of medical care. It has become apparent that whoever succeeds in creating such a system could change the world of healthcare. The basic ingredients for such a system now exists. The VA has stated that TSRI proved the concept with its UiEHR pilot project. Now, TSRI wants to move beyond the pilot to a fully integrated Mumps/Java co-located Cloud based solution. For that TSRI needs to turn to the Open Source Electronic Health Record Alliance (OSEHRA) programming community.

The following are some of the characteristics of the UiEHR solution according to TSRI:

  • “Push-Button” Conversion from Mumps to Java Cloud - Conversion of Single Systems of 2.1 to 2.5 Million lines of code in 1 hour 20 min .
  • No human intervention is required after conversion of the code to hand tune the code. Simply replace MUMPS with the JSE/JEE modules generated by the translator.
  • Original code and modernized code can interoperate and can be run at the same time.
  • Performance of the modernized code is better than MUMPS code, and is elastic and scalable in cloud-based PAAS/IAAS.
  • Translation is extremely accurate. Currently, a five sigma rate level of accuracy (less than 1 functional error per 100,000 lines of code translated) has been demonstrated.
  • MUMPS computer code can operating in parallel with the modernized code until the decision is made to pull the plug on MUMPS.


Senior health IT leadership of both the U.S. Department of Defense (DoD) and the Department of Veterans Affairs (VA) have stated that TSRI proved the concept in separate pilot projects conducted by TSRI for each organization.

TSRI's solution can create a world in which there are no barriers between diverse EHR systems. It allows organizations to move their legacy EHR systems into a unified cloud-based system - i.e. UiEHR. For more technical detail, attend the UiEHR presentation by TSRI at this year's OSEHRA Summit. You can also go to The Software Revolution Incorporated (TSRI) web site.

Sunday, August 24, 2014

Recommendations for New Legislation & Regulations Supporting the Use of 'Big Data' in Healthcare


By Marc Wine & Peter Groen

Our nation’s healthcare system is in transformation from consumers paying a fee-for-service to paying for value-based outcomes of quality services. The collection, storage, analysis, and use of 'Big Data' are key to bringing about this transformation. 

The aim of this article is to provide a high level overview and recommendations to leaders in the healthcare industry, health information technology (IT) companies, and healthcare consumers about needed changes in legislation and regulations that will help facilitate the transformation of the healthcare sector, safely and securely.

This article focuses on selected high priority areas of healthcare delivery and health information technology (IT) that require a new framework and legislation to further encourage 'open access' and use of 'Big Data' to facilitate more effective direction and decision-making.

Background

The healthcare industry is still largely unprepared to deal with the flood of 'Big Data' being generated by Electronic Health Record (EHR) and Personal Health Records (PHR) systems that now include medical images, genomic data, and even biometric data generated by smart devices - all interconnected by Health Information Exchange (HIE) networks. There is a need for new legislation, regulations, and guidelines to help more effectively collect, store, analyze, and use 'Big Data' to improve healthcare for all.

Motivated by a desire to raise awareness and initiate a more detailed discussion about the topic of 'Big Data' in healthcare by lawmakers, consumers, healthcare organizations and the health IT industry, a detailed draft report was produced entitled, “A Congressional Analysis and Recommendations for Enhancing the Use of Big Data in Healthcare.” It was hoped the report could be used to help decision-making by key organizations like the Health Information Systems Society (HIMSS), eHealth Initiative (eHi), and the 'open source' EHR development community to encourage further collaboration in developing new and innovative 'open' solutions related to 'Big Data' in healthcare. This article contains highlights from that report.


Selected 'Big Data' Projects in Healthcare
Million Veteran Program (MVP) - This is an initiative supported by the Department of Veterans Affairs (VA) to enroll one million Veterans who are active users of the VA healthcare system into a genetic epidemiology cohort. Participants give informed consent and HIPAA authorization for unrestricted use of their electronic medical record (EMR) data and completed case report form (CRF) data for IRB approved research purposes. Additionally, they agree to future re-contact for the purpose of additional data collection and donate a sample of blood for storage and testing. To date approximately 250,000 subjects have been enrolled in MVP from 50 VA Medical Centers participating in the enrollment phase of the study. The intention is to create a platform that allows researchers to use the MVP data to better understand the genetic relationships to veterans suicidal conditions, PTSD/TBI, substance abuse and Alzheimer’s Disease. (SOURCE: Massachusetts Veterans Epidemiology Research and Information Center and Million Veteran Program)
Center for Medicare and Medicaid Services (CMS) - CMS has put together a centralized approach to using Big Data and predictive analytics. As part of their Fraud Prevention System and Medicare’s anti-fraud program, assembled a repository of algorithms to target specific claim and provider types. The goal of the program is to keep individuals and companies that intend to defraud out of the system. It also equips CMS with the tools to recognize fraudulent claims and eliminate payment errors. For example Big Data tools can be used to review large healthcare claims and billing information to target payment risk associated with each provider; over-utilization of services in very short-time windows; and patients simultaneously enrolled in multiple states. (SOURCE: Government Health IT, Top 9 Fraud and Abuse Areas for Big Data, May 2012)


Key Issues & Challenges

The President's priority for the public and private sector to harness the power of 'Big Data' for boosting productivity, generating innovation and improving citizen’s health through the power of collaboration and open solutions is commendable. Continued discussions about 'Big Data' and associated federal laws and regulations will help lead to a better understanding about what next steps to take in the healthcare arena.
 
The following are some of the key issues or challenges faced with regards to the use of 'Big Data' by the healthcare industry, especially as health IT systems see more widespread use and continue to evolve - adding genomic data, medical images, and biometric data collected by a wide range of smart devices.


  • Concerns about privacy and security should receive the highest priority when crafting and issuing new law and regulations related to 'Big Data.’
  • Manipulation of information about price, quality and access to care are often kept secret by healthcare providers to maintain a competitive edge and hide many shortcomings.
  • Physician documentation styles vary substantially, making errors and omissions in data collection difficult to identify.
  • The use of health information entered by consumers and aggregated into 'Big Data' systems can intentionally and unintentionally introduce systematic errors.
  • There is a need to rationalize complex, often conflicting legal frameworks as the stakes rise and 'Big Data' becomes one of the keys to the future of healthcare.
  • Much clinical data is still stored in 'unstructured' form within EHR systems, making it difficult to access for effective use when analyzing 'Big Data'.
  • The difference between 'Open Data' and 'Big Data' needs to be clarified and understood when crafting new policies and legislation.

Without consensus on clearly defined, concrete standards and a new healthcare roadmap for use of 'Big Data' for analytical and decision-making purposes, healthcare organizations and providers are likely to generate data that is not trusted and useful.


Major Findings & Observations


Empowering people with 'Big Data' for use in generating both quality and cost efficiency  will require all special interests coming to grips with information systems and new business processes that will help move the nation’s health providers and consumers towards a continuously Learning Health System, while also expanding the adoption of better interoperable technologies.

The following are some key observations and findings related to the current state of affairs concerning the use of 'Big Data' in healthcare:

  • A look ahead promises sweeping change in healthcare as a result of the new Affordable Care Act (ACA). Healthcare reforms need to address the topic of 'Big Data'.
  • Standardization of healthcare data is still a ways off. Health IT systems and the data they contain often are comprised of incompatible formats and definitions for similar data elements.
  • 'Big Data' is also still often kept in hard-to-penetrate silos owned by companies that are less than willing to share the data collected by their proprietary systems.
  • The quality of 'Big Data' is still an issue. To derive from insights from data collected by healthcare systems, it is critical that they be accurate, complete, and semantically harmonized.
  • Lawmakers and regulators, health providers and vendors plus the consuming public have yet to fully understand the relationship between 'Open Data' and 'Big Data' in healthcare.
  • Increasingly, healthcare institutions have access to digitized patient medical records containing massive amounts of raw data.
  • Much of the available health data are in textual form. While textual data are convenient for tasks such as review by clinicians, they present significant obstacles for graphic presentation, searching, summarization, and statistical analysis.
  • An interoperable and 'open' data architecture and infrastructure are key to enabling the collection, storage, and analysis of 'Big Data' in healthcare.
  • 'Big Data' technologies have emerged that have the capability to better data mine and analyze data now accessible via emerging health information exchange (HIE) networks.
To summarize, there is a need for Congress and regulating agencies to carefully craft new legislation and regulations further enabling ways to better leverage 'Big Data' in healthcare.

Conclusions & Recommendations

The key challenge now is whether we will limit the capacities of Big Data to outmoded legal and regulatory processes of enforcement or broaden them to affirmatively improve public health and reduce costs by creating Open Big Data.

The following are some key recommendations that Congress, regulatory agencies, industry leaders, and consumer groups might want to consider related to 'Big Data' and the healthcare sector.


  • Congress and regulators should encourage increased collaboration between the public and private sector entities so new and innovative technologies, business processes and solutions related to 'Big Data' can be unleashed
  • Congress should ensure that large-hospital provider networks will take no steps to inhibit the openness of the 'Big Data' marketplace that might be harmful to consumers’ interests.
  • Congress should encourage research, analysis and meaningful use of 'Big Data' now being captured by EHR systems
  • Congress and HHS should revise HIPAA to allow and encourage further sharing of personal health data between healthcare organizations.
  • Congress should establish additional HIPAA privacy guidelines for setting up and deleting all personally identifiable data after a designated timeframe and allow consumers to opt-in as anonymous data donors to support healthcare research.
  • CMS should determine and publish national criteria and standards for the enhanced use of 'Big Data' when measuring improvements in quality of care by Medicare and Medicaid programs.
  • For healthcare consumers, Congress should continue to help create a more trusted view of pricing for common procedures across the U.S. healthcare system.
  • Congress, HHS, FDA, and other regulatory agencies strive to establish laws, regulations and guidance with regards to data being generated by the growing number of smart healthcare devices that are part of the Internet of Things (IoT).
  • Especially with regards to Public Health, CDC should issue regulations requiring more collaboration and sharing of data between public and private sector healthcare providers.
  • Congress should legislate that all data and studies generated by government funded healthcare research ought to be shared and published in 'open access' journals.
  • Congress should develop legislation that provides investments for creative partnerships between universities and health IT vendor in order to better educate healthcare professionals and administrators in the analysis and meaningful use of 'Big Data'.
Again, our nation’s healthcare system is undergoing a major transformation and the collection, storage, analysis, and use of 'Big Data' are key to bringing about this transformation. Whenever and wherever possible, 'Big Data' in healthcare must be made more transparent and 'open'. This will facilitate the need for increased collaboration and data sharing across the healthcare sector leading to the development of many new and innovative solutions that may result in improving health and healthcare for everyone.


Other selected articles on the use of 'Big Data' in healthcare include: