I’m keynoting this year’s Intersystems Global Conference on the topic of “Freeing the Data” from the transactional systems we use today such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Electronic Health Records (EHR), etc. As I’ve prepared my speech, I’ve given a lot of thought to the evolving data needs we have in our enterprises.
In healthcare and in many other industries, it’s increasingly common for users to ask IT for tools and resources to look beyond the data we enter during the course of our daily work. For one patient, I know the diagnosis, but what treatments were given to the last 1000 similar patients. I know the sales today, but how do they vary over the week, the month, and the year? Can I predict future resource needs before they happen?
In the past, such analysis typically relied on structured data, exported from transactional systems into data marts using Extract/Transform/Load (ETL) utilities, followed by analysis with Online Analytical Processing (OLAP) or Business Intelligence (BI) tools.
In a world filled with highly scalable web search engines, increasingly capable natural language processing technologies, and practical examples of artificial intelligence/pattern recognition (think of IBM’s Jeopardy-savvy Watson as a sophisticated data mining tool), there are novel approaches to freeing the data that go beyond a single database with pre-defined hypercube rollups. Here are my top 10 trends to watch as we increasingly free data from transactional systems.Continue reading…
You may have heard about the Sports Illustrated Effect, the notion that people who appear on the cover of the magazine are likely to experience bad luck, failure, or a career spiral.
Over the 30 years of my own professional life, I’ve watched many colleagues become famous, receive significant publicity, then fail to live up to the impossible expectations implied by their fame. They regress to the mean. Nature seems to favor symmetry. Things that rise slowly tend to decline slowly. Things that rise rapidly tend to drop rapidly.
Fame is usually a consequence (good or bad) of invention, innovation and accomplishment. Fame itself is generally not what motivates a person to accomplish their feats. An Olympic athlete is usually inspired because of a highly competitive spirit. An inventor is usually inspired because he/she believes there is a better way. Fame that is the consequence of a feat can affect future behavior. It can become an intoxicant and motivate someone to strive for accomplishments that keep the fame coming.
I’ve thought about my own brushes with fame.
When I was 18 and started at Stanford, I realized that my scholarships would only cover the first year of tuition. I visited the Stanford Law library, read the US tax code and wrote software for the Kaypro, Osborne 1, and CP/M computers that calculated taxes. The software shipping from my dorm room generated enough income to start a small company. When the PC was introduced, we were the first to provide such software to small businesses seeking to compute their tax obligations. By the time I was 19, I moved into the home of Frederick Terman, former Provost of Stanford, and the professor who first encouraged William Hewlett and David Packard to build audio oscillators and form a new company called HP. The story of a 19 year old running a software company and living in the basement of founder of HP was newsworthy at the time. I did interviews with Dan Rather, Larry King, and NHK TV Japan.Continue reading…
Many clinicians and hospitals have asked me about the exact steps to obtain stimulus payments.
On January 3, 2011, CMS began registering clinicians for participation in meaningful use programs. Every region of the United States has Regional Extension Centers which can help answer any questions. Here’s an overview of the steps you need to take.
1. Choose between Medicare and Medicaid programs. If you qualify, Medicaid offers greater incentives and does not require you to achieve meaningful use before stimulus payments begin.
a. To qualify for Medicaid, 30% of your patient encounters must be Medicaid patients. (20% for pediatricians)
b. To qualify for Medicare, keep in mind that meaningful use payments are made at 75% of Medicare allowable charges for covered professional services in the calendar year of payment, per the payment maximums below:
Year 1 $18,000
Year 2 $12,000
Year 3 $8000
Year 4 $4000
Year 5 $2000
Thus, a total of $44,000 is available at maximum, but could be less if your allowable Medicare charges are less than
Year 1 $24,000
Year 2 $16,000
Year 3 $10,667
Year 4 $5333
Year 5 $2667
On January 12, the Health Information Technology Policy Committee published its proposed Stage 2 and 3 Meaningful Use recommendations for public comment.
Robin Raiford from Allscripts created a Quick Guide to the recommendations, making it easy to compare Stage 1, 2 and 3 in a single PDF.
Here’s my analysis of the proposed Stage 2 and 3 criteria.
1. CPOE – Stage 1 requires more than 30% of unique patients with at least one medication in their medication list have at least one medication order entered using CPOE Stage 2 expands this to 60% of patients for at least one medication, lab or radiology order. Stage 3 expands this further to 80%. CPOE orders do not need to be transmitted electronically to pharmacies/labs/radiology departments. This is a very reasonable rate of CPOE adoption. The hardest part of implementing CPOE is getting started, which happens in Stage 1. Adding different types of transactions (without requiring electronic transmission to back end service providers) is more about workflow and behavioral change than technology change.
2. Drug-drug/drug-allergy interaction checks – Stage 1 requires that interaction technology be enabled. Stage 2 adds that it will be used for high yield alerts, with metrics for use to be defined. The idea is that many drug databases contain too many false positive interaction rules, so adoption is slowed by alert fatigue. If only high yield alerts are required (here’s what we’ve done at BIDMC ), clinicians are more likely to trust drug interaction decision support. Stage 3 adds drug/age checking (such as geriatric and pediatric decision support), drug dose checking, chemotherapy dosing, drug/lab checking, and drug/condition checking. These are all reasonable goals, but automating chemotherapy protocols is quite challenging. BIDMC built an Oncology Management System and added a full time research nurse to ensure all chemotherapy protocols are updated and accurate. It may be asking too much to require chemotherapy dosing decision support nationwide by 2015.
As State Health Information Exchanges and Federal efforts (NHIN Connect/NHIN Direct) implement the data sharing technology that will enable all providers in the country to achieve Meaningful Use Stage 1, I’m often asked “but when will this healthcare information exchange technology be able to retrieve all my records from everywhere when I’m lying unconscious in the Emergency Department and cannot give a history?”
Here are my thoughts about the trajectory we’re on and how it will lead us to supporting the “Unconscious in the ED” use case.
Meaningful Use Stage 1 is about capturing data electronically in EHRs. Getting healthcare data in electronic form is foundational to any data exchanges. By 2011 we should have medication lists, problem lists, allergies, and summaries available from EHRs.
The data exchanges in Stage 1 are simple pushes of data from point A to point B – from provider to public health, from provider to provider, and from provider to pharmacy. There is no master patient index, no record locator service, and no centralized database containing everyone’s lifetime health record.
1. The Emergency Department is mentioned in 9 Core Measures and 3 Menu Measures, yet industry discussions seem to focus on the ED for CPOE and Discharge instructions. What functions do ED Information Systems need to support? Are these functions for just admitted patients or all ED Patients?
In my conversations with CMS, I believe that CMS will be issuing a corrections notice to clarify the role of the ED in the rule.Continue reading…
Whether it’s religion, politics, or even my local administrative leadership, authority figures must earn my trust.
Earning that trust is not easy. As folks who work closest with me know, I believe that much of Dilbert is based on true case studies.
Over the past year, I’ve worked very closely with many people at ONC – David Blumenthal, John Glaser, Judy Sparrow, Farzad Mostashari, Chuck Friedman, Carol Bean, Doug Fridsma, Chris Brancato, Jonathan Ishee, Arien Malec (on loan to ONC for 8 months), and Jodi Daniel. I’ve worked with HHS CTO Todd Park. I’ve worked with US CTO Aneesh Chopra.Continue reading…
Every year, I describe my top 10 impressions from HIMSS. Here’s my summary of the event for 2010
I’ve just finished my day in Atlanta and am beginning a commute to Tokyo.
Every year, I describe my top 10 impressions from HIMSS. Here’s my summary of the event for 2010Continue reading…
John Halamka is the CIO at Beth Israel Deconess Medical Center and the author of the popular “Life as a Healthcare CIO” blog, where he writes about technology, the business of healthcare and the issues he faces as the leader of the IT department of a major hospital system. He is a frequent contributor to THCB.
I recently wrote a Computerworld Column about Email Overload.