My recent posting describing my updated study of the health IT workforce shows that this is a great time to work in operational health IT and informatics settings. Many of us, however, work as faculty or in other professional roles in academic health science centers, a smaller but critically important part of the informatics workforce. What are the prospects for those in academic informatics?
I would argue they are excellent. There are great opportunities now both for those who follow the traditional academic researcher/educator pathway as well as for those who focus their involvement in the more operational activities in academic health science centers.
For those following the more conventional faculty pathway, the grant funding situation is currently pretty good. While the main supporter of basic informatics research, the National Library of Medicine (NLM), has a small research budget, it has grown 14% with the increased federal funding to the National Institutes of Health (NIH) in the last couple years. Fortunately, informatics researchers have more options. Despite attempts in some political quarters to de-fund the Agency for Healthcare Research & Quality (AHRQ), the agency continues to pursue and fund its research objectives, a decent portion of which involves informatics innovation. Likewise, the other institutes of the NIH, including those that are disease-oriented, offer opportunities for research that includes informatics activities. This includes not only the big initiatives, such as the AllOfUs project, but day-to-day work with others, such as the National Sleep Research Resource. There are also research funding opportunities from foundations, industry, and others.
Of course, one fortunate aspect of being academic informatics faculty is that activities are not limited to those focusing mainly on research. There are other opportunities in teaching (including beyond those studying informatics, such as healthcare professional students) and operational work (supporting and innovating in all of the missions of academic medical centers, which include clinical care, research, and education). Academic informaticians are often involved implementation of operational systems, especially those supporting healthcare delivery and research. Given the growth of informatics and data science, there are likely to be teaching opportunities for those of us who enjoy teaching our area of expertise to clinicians and others who work in healthcare.
For all of these reasons, I am pretty bullish on careers in academic informatics. While no career pathway in any field is a guarantee of success these days, there are plenty of opportunities for those seeking academic careers in informatics.
This blog maintains the thoughts on various topics related to biomedical and health informatics by Dr. William Hersh, Professor, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University.
Tuesday, October 30, 2018
Friday, October 12, 2018
What are the Optimal Data Science and Machine Learning Competencies for Informatics Professionals?
Exactly 20 years ago, I organized a panel at the American Medical Informatics Association (AMIA) Annual Symposium that attracted so large an audience that the crowd spilled out of the room into the hallway. Entitled, What are the Optimal Computer Science Competencies for Medical Informatics Professionals?, the panel asked how much knowledge and skills in computer science were required to work professionally in informatics. In the early days of informatics, most informaticians had some programming skills and often contributed to the development of home-grown systems. Some educational programs, such as the one at Stanford University, had required courses in assembly language. (I took an assembler course myself during my informatics fellowship in the late 1980s.)
But as academic informatics systems grew in scope and complexity, they needed more engineering and hardening as they became mission-critical to organizations. At the same time, there was a recognized need for attention to people and organizational issues, especially in complex adaptive settings such as hospitals. Over time, most professional work in informatics has shifted from system building to implementing commercial systems.
With these changes, my evolving view has been that although few informatics professionals perform major computer programming, there is still value to understanding the concepts and thought process of computer science. While plenty of students enter our graduate program at Oregon Health & Science University with programming skills, our program will not turn those without programming skills into seasoned programmers. But I still believe it is important for all informatics professionals to understand the science of computing, even at the present time. This includes some programming to see computing concepts in action.
A couple decades later, I find myself asking a related question, which is, how much data science and machine learning is required of modern informatics professionals? Clearly data science, machine learning, artificial intelligence, etc. are very prominent now in the evolution healthcare and biomedical science. But not everyone needs to be a "deep diver" into data science and machine learning. I often point this out by referring to the data analytics workforce reports from a few years ago that note the need for a five- to ten-fold ring of people who identify the needs, put into practice, and communicate the results of the deep divers [1, 2]. I also note the observation of data analytics thought leader Tom Davenport, who has written the importance of the roles of "light quants" or "analytical translators" in data-driven organizations (such as healthcare)[3].
Thus to answer my question in the title of this post, competence in data science and machine learning may be analogous to the answer to the computer science question of a couple decades ago. Clearly, every informatician must have basic data science skills. These include knowing how to gather, wrangle, and carry out basic analysis of data. They should understand the different approaches to machine learning, even if they do not necessarily understand all of their deep mathematics. And of course they must critically know how to apply data science and machine learning in their everyday professional practice of informatics.
References
1. Manyika, J, Chui, M, et al. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.
2. Anonymous (2014). IDC Reveals Worldwide Big Data and Analytics Predictions for 2015. Framingham, MA, International Data Corporation. http://bit.ly/IDCBigDataFutureScape2015.
3. Davenport, T (2015). In praise of “light quants” and “analytical translators”. Deloitte Insights. https://www2.deloitte.com/insights/us/en/topics/analytics/new-big-data-analytics-skills.html.
But as academic informatics systems grew in scope and complexity, they needed more engineering and hardening as they became mission-critical to organizations. At the same time, there was a recognized need for attention to people and organizational issues, especially in complex adaptive settings such as hospitals. Over time, most professional work in informatics has shifted from system building to implementing commercial systems.
With these changes, my evolving view has been that although few informatics professionals perform major computer programming, there is still value to understanding the concepts and thought process of computer science. While plenty of students enter our graduate program at Oregon Health & Science University with programming skills, our program will not turn those without programming skills into seasoned programmers. But I still believe it is important for all informatics professionals to understand the science of computing, even at the present time. This includes some programming to see computing concepts in action.
A couple decades later, I find myself asking a related question, which is, how much data science and machine learning is required of modern informatics professionals? Clearly data science, machine learning, artificial intelligence, etc. are very prominent now in the evolution healthcare and biomedical science. But not everyone needs to be a "deep diver" into data science and machine learning. I often point this out by referring to the data analytics workforce reports from a few years ago that note the need for a five- to ten-fold ring of people who identify the needs, put into practice, and communicate the results of the deep divers [1, 2]. I also note the observation of data analytics thought leader Tom Davenport, who has written the importance of the roles of "light quants" or "analytical translators" in data-driven organizations (such as healthcare)[3].
Thus to answer my question in the title of this post, competence in data science and machine learning may be analogous to the answer to the computer science question of a couple decades ago. Clearly, every informatician must have basic data science skills. These include knowing how to gather, wrangle, and carry out basic analysis of data. They should understand the different approaches to machine learning, even if they do not necessarily understand all of their deep mathematics. And of course they must critically know how to apply data science and machine learning in their everyday professional practice of informatics.
References
1. Manyika, J, Chui, M, et al. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.
2. Anonymous (2014). IDC Reveals Worldwide Big Data and Analytics Predictions for 2015. Framingham, MA, International Data Corporation. http://bit.ly/IDCBigDataFutureScape2015.
3. Davenport, T (2015). In praise of “light quants” and “analytical translators”. Deloitte Insights. https://www2.deloitte.com/insights/us/en/topics/analytics/new-big-data-analytics-skills.html.
Tuesday, October 9, 2018
A Meaningful End to “Meaningful Use?”
The era of meaningful use came to a relatively quiet end this summer with the release of the Final Inpatient Prospective Payment Systems rule by the Center for Medicare and Medicaid Systems (CMS) this past August. The rule put into place most of what had been in the proposed rule earlier in the year. Although the rule has much detail on what healthcare organizations must achieve to receive incentive payments and/or avoid penalties, a large symbolic change is the renaming of the Medicare and Medicaid Electronic Health Record (EHR) Incentive Programs now be called Promoting Interoperability Programs. The "meaningful use" moniker goes away, although under the new program, eligible professionals and hospitals still must demonstrate they are "meaningful users" of health information technology.
As someone who had a front-row seat in meaningful use and how it impacted the informatics world (in my case more teaching about it than being in the trenches implementing it), it is the end of an era that brought our field to national visibility. There is some success to be celebrated by the fact that 96% of hospitals and 85% of office-based clinicians have adopted some form of EHR. Overall, the new rules seem logical and fair, although some would argue that incentive payments should be based more on outcomes than process measures. In any case, there is still important work ahead as we step up to challenge to making EHR systems better and leveraging the data in them to truly benefit health and healthcare.
Unlike in the past, when summaries of the updates were released with great fanfare by multiple sources, there are few summaries of the new rule that provide enough content to understand the details without having to read the hundreds of pages in the government publication. Two good sources I have found are:
The new CMS rule now applies a similar approach to eligible hospitals. The new rule groups Promoting Interoperability into four overall objectives, each of which has one or more measures and a maximum number of points for achieving them. The new rule also streamlines some of the quality reporting measures required by the program as well as limits the reporting period to one quarter of the year.
A final change in the new rule is the requirement that systems use the 2015 Edition Certified EHR Technology (CEHRT) criteria to be eligible for the program. One key requirement of the 2015 CEHRT edition is the implementation of an application programming interface (API) that can (with appropriate authentication and security) access data directly in the EHR. Most vendors are implementing this capability using the emerging Fast Healthcare Interoperability Resources (FHIR) standard. Probably the best-known (but certainly not the only) application of this is the Apple Health app that allows patients to download the so-called Argonaut data set of 21 data elements.
The new Promoting Interoperability measures include:
1. e-Prescribing (1 required, 2 optional measures in 2019 that will be required in 2020)
https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/
It is hard not to wax somewhat nostalgic about these changes, especially in this blog that started about the time of the introduction of the Health Information Technology for Clinical and Economic Health (HITECH) Act that seems like eons ago. Although the goal was not just to put computers into hospitals and clinicians’ offices, that is an accomplishment and hopefully lays the foundation for improving healthcare and leveraging data going forward.
As someone who had a front-row seat in meaningful use and how it impacted the informatics world (in my case more teaching about it than being in the trenches implementing it), it is the end of an era that brought our field to national visibility. There is some success to be celebrated by the fact that 96% of hospitals and 85% of office-based clinicians have adopted some form of EHR. Overall, the new rules seem logical and fair, although some would argue that incentive payments should be based more on outcomes than process measures. In any case, there is still important work ahead as we step up to challenge to making EHR systems better and leveraging the data in them to truly benefit health and healthcare.
Unlike in the past, when summaries of the updates were released with great fanfare by multiple sources, there are few summaries of the new rule that provide enough content to understand the details without having to read the hundreds of pages in the government publication. Two good sources I have found are:
- AMIA - https://www.amia.org/sites/default/files/FY19-IPPS-Final-Rule-Detailed-Summary.pdf
- Healthcelerate - https://www.healthcelerate.com/s/Healthcelerate-Promoting-Interoperability-Guide_August-2018.pdf
The new CMS rule now applies a similar approach to eligible hospitals. The new rule groups Promoting Interoperability into four overall objectives, each of which has one or more measures and a maximum number of points for achieving them. The new rule also streamlines some of the quality reporting measures required by the program as well as limits the reporting period to one quarter of the year.
A final change in the new rule is the requirement that systems use the 2015 Edition Certified EHR Technology (CEHRT) criteria to be eligible for the program. One key requirement of the 2015 CEHRT edition is the implementation of an application programming interface (API) that can (with appropriate authentication and security) access data directly in the EHR. Most vendors are implementing this capability using the emerging Fast Healthcare Interoperability Resources (FHIR) standard. Probably the best-known (but certainly not the only) application of this is the Apple Health app that allows patients to download the so-called Argonaut data set of 21 data elements.
The new Promoting Interoperability measures include:
1. e-Prescribing (1 required, 2 optional measures in 2019 that will be required in 2020)
- e-Prescribing
- Query of Prescription Drug Monitoring Program (PDMP)
- Verify Opioid Treatment Agreement
- Support Electronic Referral Loops by Sending Health Information
- Support Electronic Referral Loops by Receiving and Incorporating Health Information
- Provide Patients Electronic Access to Their Health Information
- Syndromic Surveillance Reporting
- Immunization Registry Reporting
- Electronic Case Reporting
- Public Health Registry Reporting
- Clinical Data Registry Reporting
- Electronic Reportable Laboratory Result Reporting
https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/
It is hard not to wax somewhat nostalgic about these changes, especially in this blog that started about the time of the introduction of the Health Information Technology for Clinical and Economic Health (HITECH) Act that seems like eons ago. Although the goal was not just to put computers into hospitals and clinicians’ offices, that is an accomplishment and hopefully lays the foundation for improving healthcare and leveraging data going forward.