Healthcare & Technology

Medicine's Future? Tech Trends in Medicine

Link: Medicine's future?

Daniel Kraft: Medicine's future? There's an app for that

Daniel Kraft offers a fast-paced look at the next few years of innovations in medicine, powered by new tools, tests and apps that bring diagnostic information right to the patient's bedside.

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Daniel's presentation is fascinating - there are a significant number of new technology applications on the market, some still in clinical trials. However, he shows us the potential changes that can come from their use in medicine with real examples, including those for Telemedicine.

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Informatics and Big Data for ACOs

Internet resources can provide benchmarks and analysis for Medicare Populations.

The CMS Chronic Conditions Data Warehouse website is located at www.ccwdata.org. As described on the site, the warehouse provides Medicare and Medicaid beneficiary claims and assessment data, linked by beneficiary across the continuum of care.  This website provides dashboards that provide the ability to look at chronic conditions, by age, by location, or demographics. Other websites provide dashboards of health outcomes by state and zip code.

The amount of data collected by ACOs will grow rapidly over time and become a critical source for measuring quality of care, costs and operational efficiency.  Generally, ACOs need to either build or outsource development of a central data warehouse to gather, store and analyze data. Development of a data warehouse happens to be one of the challenges of a big data implementation. Three characteristics of big data include size, complexity, and technologies. EMRs are optimized for rapid data input of transactions. Often there are a number of standard reports that can be produced for a given period of time, or as of the date they are run. A key aspect of transaction systems is also the amount of “care and feeding” they require. For example, updates, backups, disaster recovery, etc.

 

Informatics and Technology in Accountable Care Organizations

Accountable Care Organizations have the task of gathering patient data from wherever it may originate. Care Coordination may also occur wherever the patient is located. Successful strategies for care coordinators begin even before a patient is discharged from the hospital.

Managing large amounts of data requires a robust infrastructure of databases, networks, and the ability to exchange data among ACOs. Analyses of large databases typically use regression analysis techniques for prediction and forecasting. Regression analysis may identify clinical predictors of health that alert staff to declining health situations or when interventions are needed. One of the biggest successes noted by ACO executives was to use predictive analytics to identify high-risk patients for proactive care.

Challenges and barriers related to informatics and technology are often reflective of the knowledge needed to use tools to analyze data. Other barriers include financial investment and operating costs. In addition, hospitals and clinics must be able to exchange patient data for episodes of care in the emergency department, in-patient stays, or clinic visits. Many ACOs use HIEs or Health Information Exchanges to share data not only from hospitals and clinics, but also from specialty providers. CMS Claims data is received monthly and should be stored in a database for analysis. Registries, CMS Reporting, GPRO and performance data sites require secure Internet access. The HITECH Act provides regulations to ensure Privacy and Security of protected health information using various technologies. Of course it should be noted that none of these technologies come without their share of operating costs and can become barriers when knowledgeable staff are not available.

 

How Providers Can Succeed Under Health Reform

Link: The Camel's Nose is in the Tent: Health Care Reform & the Joslin Affiliate

David Harlow presents an interesting webinar on the Affordable Care Act's implementation of Accountable Care Organizations and the Medicare Shared Savings Program. He states, "ACOs exist in the commercial insurance world as well, as part of the broader trend towards value-based payment." Just click on the link at the top of this post to listen to this informative webinar.

In a recent article from mHealth News, ACOs are reaping mHealth benefits. The 13-hospital Franciscan Alliance Accountable Care Organization, has achieved a "5 percent readmission rate (the national average for a Medicare population is 20 percent), medication reconciliation rates above 40 percent, and a patient retention rate of 95 percent."

In an article by Debbie Hacker and Karen Walker in the American Journal of Public Health on ACOs, they state, "Coordinated care helps ensure that patients, especially the chronically ill, get the right care at the right time, with the goal of avoiding unnecessary duplication of services and preventing medical errors. When an ACO succeeds in both delivering high-quality care and spending health care dollars more wisely, it will share in the savings it achieves for the Medicare program."

 

Informatics, Big Data and Accountable Care Organizations

This section will discuss Health Informatics and targeted Quality Measures for ACOs.

a. Performance Standards

b. Quality Metrics

c. Analytics and Big Data

Health Informatics includes resources and methods to manage health information and the tools and infrastructure to analyze data.  Analytical tools may come in the form of EHRs, data exchanges, data warehouses, database repositories, paper documents, networks and protected internet access.

Due to the nature of data collected by ACOs and the need to share data among providers, there can be significant challenges. Some of these challenges come in the form of "Big Data."

Big data essentially implies a database schema that is suitable for answering queries that may or may not have been anticipated at the time of schema design.

One of the challenges in a big data implementation is recognizing that numerous design considerations are far different than what normally apply to a transaction system.

• For example, transaction systems, usually have a relational database schema in what is called 3NF or what is called Third Normal Form. Whereas data warehousing systems generally have a star schema.
• Although these two types of design system are as different as night and day, a big data implementation is more akin to a data warehouse on steroids.
• Very few people understand how to do star schemas correctly. Star schema design is usually performed by specialized consultants working within particular niches. If an organization gets the database design wrong, then it is likely that the entire project fails. DOA.
• Further, in a data warehouse has more moving parts than a transactional system. Usually you are required to integrate data from various systems and therefore data identify issues arise and must be resolved. For example different transaction systems often call the same data element by different names.
• Further you have to build a robust scalable extract transform and load process that is capable of dealing with exceptions in an effective manner otherwise you have your traditional garbage in garbage out and users quickly begin to doubt the veracity of the data.

Performance Issues

• Transaction systems are optimized for updates whereas data warehousing and data analytics are optimized for query performance. Given the size of data warehousing and data analytic systems hardware and parallelism and other advanced techniques may need to be applied.
• In short data warehousing an analytic systems are complex and expensive to develop an the information highway is littered with failed projects. Primary due to teams not having the necessary talent available to build these types of complex systems.
• It takes an entirely different skill set to build these types of systems and most IT organizations do not have the necessary in-house talent.

Big data implies much more than data warehousing.

• BD systems are by definition generally orders of magnitude larger in terms of data than DW or analytic systems.
• BD systems usually have to deal effectively with both structured and unstructured data which means that you are likely going to need more than a relational database to meet your objectives. BD may require NOSQL databases to deal with unstructured data, yet another level of complexity.
• To implement successfully, BD require world-class engineers and even that is not enough.

Why?

• Because engineers generally do not have the industry expertise to build the systems without world—class analysts that can bridge the gaps between IT and Clinical staff.
• If past is prologue, then for every successful BD project you can see dozens of failed projects.
• Although BD will transform healthcare it is no silver bullet and there is no predicting who the winners and losers might be.