Tuesday, July 16, 2019

The Next Chapter in Continuing Education for Informatics

This week, the Department of Medical Informatics and Clinical Epidemiology (DMICE) of Oregon Health & Science University (OHSU) launched a new annual continuing education (CE) activity in clinical informatics. With the first offering of the OHSU Annual Update in Clinical Informatics, a selection of important topics will be covered to provide an update for all clinical informatics professionals. For physicians, the course will provide continuing medical education (CME) credit. For physicians certified in the clinical informatics subspecialty, the course will provide MOC-II/LLSA credits.

The field of clinical informatics (and closely related health informatics) is a growing profession that plays an important role in healthcare and other health-related areas [1]. Informatics professionals insure that data and information are used most effectively to improve healthcare, public health, individual health, and research. The certification initially of physicians [2] and soon others in the field [3] requires that all informatics professionals maintain and expand their knowledge and skills.

This course builds off the extensive informatics education offerings of DMICE, from our biomedical informatics graduate program that has awarded 831 degrees and certificates over more than 20 years to our other innovative activities such as the AMIA 10×10 (“ten by ten”) program, the development of online learning in informatics, and launching one of the first clinical informatics subspecialty fellowships for physicians [4].

The learning activity consists of 7 modules that are estimated to take a total of 8 hours to complete. The activity is completely online, and consists of lectures and self-assessment quizzes. The topics for the 2019 annual update were selected by DMICE faculty. Topics for future annual updates will be chosen with input from those who completed previous annual update courses.

After taking this learning activity, clinical informatics professionals will be able to (1) be aware of current advances in clinical informatics. (2) apply these advances to their professional practice, and (3) meet required competencies that are related to the domain of clinical informatics in the practice of their profession.

The activity will consist of a number of talks given by DMICE faculty that will focus on recent developments in the field. The activity will be hosted on OHSU’s Sakai learning management system as enduring learning material. Once learners enroll in the activity, they will have access to Sakai and be able to complete the activity and evaluations at their own pace. Each talk will be accompanied by a post-test (multiple choice), and learners will also need to complete a course evaluation at the end of their learning. The 2019 course activities must be completed by June 30, 2020.

The topics covered in this year’s offering of the course include:
  • Operational Clinical Informatics
  • Organizational Behavior
  • Data Science and Machine Learning
  • Clinical Research Informatics
  • Informatics Education
  • SMART on FHIR
  • Nursing Informatics
Details of this online CE experience are available at: https://www.ohsu.edu/school-of-medicine/medical-informatics-and-clinical-epidemiology/ohsu-annual-update-clinical

This is not the only continuing education activity in clinical informatics that will be offered by OHSU. In the coming year, we will also offer for CME and ABPM MOC-II credit our monthly clinical informatics journal clubs and grand rounds.

References

1. Fridsma, D. (2019). Strengthening our profession by defining clinical and health informatics practice. Journal of the American Medical Informatics Association, Epub ahead of print.
2. Detmer, D., & Shortliffe, E. (2014). Clinical informatics: prospects for a new medical subspecialty. Journal of the American Medical Association, 311, 2067-2068.
3. Gadd, C., Williamson, J., Steen, E., & Fridsma, D. (2016). Creating advanced health informatics certification. Journal of the American Medical Informatics Association, 23, 848-850.
4. Longhurst, C., Pageler, N., Palma, J., Finnell, J., Levy, B., Yackel, T., . . . Hersh, W. (2016). Early experiences of accredited clinical informatics fellowships. Journal of the American Medical Informatics Association, 23, 829-834.

Monday, July 8, 2019

Kudos for the Informatics Professor - Winter/Spring 2019 Update

I have had a busy but productive early 2019, with invited talks, publications, and other happenings.

I gave a few invited talks:
The latter was a real honor, as it took place at my medical school alma mater, University of Illinois Chicago, and it was fun to see both informatics colleagues as well as some former classmates who attended the lecture.

I also had the opportunity to attend the inaugural induction of Fellows of the American Medical Informatics Association (FAMIA) at the AMIA Clinical Informatics Conference in Atlanta, GA on May 1, 2019. The initial group of FAMIA included 15 alumni and faculty of the OHSU Biomedical Informatics Graduate Program, comprising over 11% of the inaugural fellows. Below is a picture of OHSU alumni and myself who attended the induction ceremony.



I continue to serve on several scientific advisory boards:
  • Pan African Bioinformatics Network for H3Africa (H3ABionet), which provides bioinformatics support for the Human Heredity and Health in Africa Project (H3Africa). I will be attending my second meeting of the board in Cape Town, South Africa in July.
  • RCMI Multidisciplinary And Translational Research Infrastructure EXpansion (RMATRIX), a translational research center of the John A. Burns School of Medicine of the University of Hawaii. The grant funding of this is ending, so this board will also be ending.
  • ECRI Guidelines Trust (EGT) Technical Advisory Panel (TAP), a publicly available web-based repository of objective, evidence-based clinical practice guideline content that succeeds the Agency for Healthcare Quality & Research (AHRQ) National Guidelines Clearinghouse. (I also served on the Technical Expert Panel of National Practice Guidelines Clearinghouse when it was developed and launched by AHRQ from 1998-2002.)
I am also co-author on a couple papers that were published:
I also had the pleasure, as I do every June, of seeing a new group of graduates from the OHSU Biomedical Informatics Graduate Program. This year saw 45 new alumni of the program honored. With these new graduates, the program has now awarded a total of 831 degrees and certificates dating back 22 years to the first graduates in 1998 (who started when the program launched in 1996). As some have completed more than one program degree or certificate (e.g., the Graduate Certificate and Master’s or the Master’s and PhD; one person has done all three!), the program has a total of 746 alumni.

Finally, AMIA has produced and posted videos for several of the courses in the 10x10 program. A video of myself describing the OHSU course has been posted on the main page for the program on the AMIA Web site.

Monday, June 24, 2019

Introducing Informatics.Health

About a year ago, the Internet Assigned Numbers Authority (IANA) launched a new set of top-level Internet domains. One of these was .health. When to my surprise, the informatics.health domain name was still available, I immediately grabbed it. I am now pleased to launch my first use of the domain name, which is a re-direct to my well-known site, What is Biomedical and Health Informatics?

In honor of this launch, I have completely updated the "What is...?" site, which I use to both provide an overview of the field to those interested and also demonstrate the online learning technologies that we use in our Biomedical Informatics Graduate Program at OHSU.

The main part of the site consists of the following lecture segments (time in parentheses):
  • What is Biomedical and Health Informatics (1)? (23:30)
  • What is Biomedical and Health Informatics (2)? (17:41)
  • A Short History of Biomedical and Health Informatics (21:33)
  • Resources for Field: Organizations, Information, Education (24:37)
  • Examples of the Electronic Health Record (EHR) (24:08)
  • Data Science and Machine Learning (1) (14:26)
  • Data Science and Machine Learning (2) (20:09)
  • Information Retrieval (Search) (24:05)
  • Information Retrieval Content (29:26)
The site also contains links to books, articles, organizations, and educational Web site.

Over time, I will probably move the site to a new server, and eventually I may develop different content for it. However, I will always want the site to be an overview of the biomedical and health informatics field. 

Saturday, June 22, 2019

Recovering from a Computer Demise, 21st Century Edition


My professional and personal lives have involved computer use for many decades. As time has gone on, the proportion of my life tied to computing devices has increased. Whereas in the early days I mainly saved programs and simple documents, my life is now intertwined with computers, smartphones, and tablets; covering virtually everything I do professionally and also a large amount of personal activities, from pictures to music to documents and more.

Although computers are more reliable than ever, the impact of a failure is more catastrophic in the present when so much of one's life is tied up in them. The recent sudden demise of my MacBook Pro reminded me of this.

Back in the early days, as well as now, the most critical problem where is a computer failure is loss of one's data. Because data loss was more common in those early days, I have always backed up my data frequently. In modern times, the easiest way to do this as a Mac user is through the use of Time Machine, which is built into the operating system. I keep two hard disks for this purpose, one to take with me whenever I travel and another to stay home in case everything is lost or stolen on a trip. Then and now, I have always backed up my data about once a day. This habit fortunately minimized the impact of my recent computer demise.

I have actually experienced only a handful of computer failures in my decades of using them. But a few weeks ago, while flying home from Singapore, fortunately on the last leg of the trip from San Francisco to Portland, my MacBook Pro just died. No amount of trying to reset the System Management Controller (SMC) or anything else helped. The battery was far from dead, so plugging in the computer did not help either. The death was verified by the OHSU IT department after I landed and brought it in to them.

Fortunately my department had a recently re-imaged MacBook Pro for me to use immediately. But most fortunately, I had backed up my now-dead machine about 24 hours earlier.

When I get a new Mac, I generally prefer not to restore the entire computer image in Time Machine. Even though it would be faster, I know of those who have had problems with this approach, and I prefer to re-build my machine by re-installing the data and then the individual apps. I also like the opportunity to do some "housecleaning" to get rid of applications I am not actively using and mostly clutter machine.

(I also have a systematic method for backing up all data I want to maintain in the long run. I have been doing this as well since the late 1980s and have an archive of essentially my entire career, even though some files from those early days no longer have applications that can open them. Microsoft Office, for example, will not open those Word/Excel/Powerpoint-format files from those times, but the files can be opened by a text editor.)

With the new MacBook Pro, I was quickly able to restore my data from my Time Machine disk, which gave me all of my data from about 24 hours prior. This meant I would be losing all work I had done in the last 24 hours since that backup, which was not insubstantial, since I had been working during my last hours in Singapore and then my long trans-Pacific flight. I was able to retrieve a few things I had done within those 24 hours, for example documents I composed and sent by email, which were in the outbox of my mail client. (Doesn’t hurt to have had wifi on my long flight!)

It generally takes me a couple weeks to get a new computer fully restored from a prior one, and this case was no different. The MacBook Pro was sent to Apple for repairs and they had to replace the entire innards, so there was no way to recover anything from the old computer. But due to careful backing up and other processes, my computer demise was fortunately not too painful, and I re-learned the lesson of regularly backing up one's work.

I know there are also in modern times some processes that eliminate the need for users to actively back up their work, such as to a cloud-based location. But even this would be imperfect for me, since my Internet access is not yet completely ubiquitous (such as when the airplane wifi does not work or a local connection is too expensive or otherwise not available). So I imagine that my habit of regularly backing up my data will be a good one to keep for some time to come.

Thursday, April 11, 2019

Beyond Images and Waves: How Will Deep Learning Benefit Health or Healthcare?


Scarcely a week goes by without another study published of a deep learning algorithm that achieves accuracy comparable to or sometimes better than human experts. The vast majority of these studies focus on some application to diagnostic imaging or waveform interpretation. There is no question that the practice of medicine will be highly impacted by these systems, especially for practitioners in fields that directly use them, such as radiologists, pathologists, dermatologists, and so forth.

What about deep learning applied to other areas of medicine? One group of studies has applied deep learning to retrospective electronic health record (EHR) data. A number of studies have shown impressive abilities to use EHR data to predict or diagnose:
  • Several dozen diseases [1]
  • Length of stay, mortality, readmission, and diagnosis at two large medical centers [2]
  • Prognosis in palliative care [3]
  • 30-day readmission in heart failure [4]
  • Patient mortality from coronary artery disease more accurately than traditional cardiovascular risk models [5]
  • Early risk of chronic kidney disease in patients with diabetes [6]
  • Many pediatric diagnoses at a major referral center [7]
  • Clinical outcomes in rheumatoid arthritis [8]
One can see obvious use cases for these types of systems, such as being able to intervene when patients might fare poorly when hospitalized or are at risk for readmission or more serious outcomes after they are discharged. But making a faster or more accurate diagnosis from an imaging or waveform study is a different matter than trying to determine the best use of an algorithm that tells a clinical team that a patient may be heading toward a bad outcome. When do we apply the results of the system? Once we intervene, does this change the nature of future recommendations? These are fascinating research questions, but also big unknowns in terms of how to apply such data in the clinical setting.

I wrote back in 2014, and more recently in 2017, that these systems must go beyond their ability to predict and actually be used in the context of prescriptive systems that results in better outcomes for the patient and/or the healthcare system. I hope to see studies going forward where these sorts of systems become part of the patient care team, and lead to demonstration of Friedman’s Fundamental Theorem of Informatics [9], which is that humans aided by machines do better than humans or machines alone. A nice roadmap for such studies was recently published that notes the need for studies that include meaningful endpoints, appropriate benchmarks, transportable to other settings and systems, and including legal and ethical monitoring [10].

This sentiment is echoed in the excellent new book by Dr. Eric Topol, Deep Medicine [11]. Dr. Topol raises the notion of deep learning benefitting patient care in even additional ways. One could solve a problem that has vexed healthcare with the widespread adoption of EHRs, which is the introduction of a third entity into the patient-physician encounter, namely the computer. Clinicians now unfortunately spend too much time “feeding the beast,” especially when that beast distracts from the patient and has additional compliance and billing burdens, contributing heavily to our epidemic of burnout in clinicians [12].

Perhaps one area where deep learning might help reduce the clinician burden is in data entry. An intriguing new study was recently published by one of the same authors who contributed some of the EHR suites above, automatically charting symptoms from patient-physician conversations [13]. One also wonders whether the level of a patient visit for billing purposes, currently determined by the presence of various elements document in the medical record, could be replaced by other data easy to collect in the modern medical office, such as time spent with the patient, time with other aspects of care, and other requirements of care. While we may never be able to achieve a “computer-free” patient examination room, we will hopefully find ways to reduce its impact and burden.

Dr. Topol suggests a second major area of benefit for deep learning, which is monitoring patients in much more depth than our current approach to providing episodic healthcare. Of course, this must be done in ways that provide actionable information presented in ways that do not further overburden clinicians. But it is not beyond the pale to envision his view of algorithms the coalescence of -omics, personal sensors, physiological measurements, healthcare, and public health data coming together to give diagnostic, therapeutic, and prognostic advice. There are still many issues around cost, ethics, practicality, and so forth, so the benefits will need to outweigh the risks.

Thus, while we will likely see machine assistance in diagnosis from images and wave forms in the near future, the impact of what deep learning may do with patient data in the EHR and with the patient will likely take longer. I see exciting opportunities for research and development focus on how to prospectively determine how such systems fit into the workflow of patient interaction within and outside the healthcare system. A nice overview

References
1. Miotto, R, Li, L, et al. (2016). Deep Patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports. 2016: 26094. https://www.nature.com/articles/srep26094
2. Rajkomar, A, Oren, E, et al. (2018). Scalable and accurate deep learning for electronic health records. npj Digital Medicine. 1: 18. https://www.nature.com/articles/s41746-018-0029-1
3. Avati, A, Jung, K, et al. (2018). Improving palliative care with deep learning. BMC Medical Informatics & Decision Making. 18: 122. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-018-0677-8
4. Golas, SB, Shibahara, T, et al. (2018). A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Medical Informatics & Decision Making. 18: 44. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-018-0620-z
5. Steele, AJ, Denaxas, SC, et al. (2018). Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PLoS ONE. 13(8): e0202344. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202344
6. Ravizza, S, Huschto, T, et al. (2019). Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nature Medicine. 25: 57-59.
7. Liang, H, Tsui, BY, et al. (2019). Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature Medicine. 25: 433-438.
8. Norgeot, B, Glicksberg, BS, et al. (2018). Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Network Open. 2(3): e190606. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2728001
9. Friedman, CP (2009). A 'fundamental theorem' of biomedical informatics. Journal of the American Medical Informatics Association. 16: 169-170.
10. Parikh, RB, Obermeyer, Z, et al. (2019). Regulation of predictive analytics in medicine. Science. 363: 810-812.
11. Topol, E (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York, NY, Basic Books.
12. Gardner, RL, Cooper, E, et al. (2019). Physician stress and burnout: the impact of health information technology. Journal of the American Medical Informatics Association. 26: 106-114.
13. Rajkomar, A, Kannan, A, et al. (2019). Automatically charting symptoms from patient-physician conversations using machine learning. JAMA Internal Medicine. Epub ahead of print.

Friday, March 29, 2019

Data Science, Biomedical Informatics, and the OHSU Department of Medical Informatics & Clinical Epidemiology

(The following is reposted from Health, Data, Information and Action, the blog of the Oregon Health & Science University Department of Medical Informatics & Clinical Epidemiology.)

Data Science is a broad field that intersects many other fields within and outside of biomedicine and health, including biomedical informatics. Data science is certainly an important component of research and educational programs in the OHSU Department of Medical Informatics & Clinical Epidemiology (DMICE).

What exactly is data science? There are many methods, but one consensus is, “the multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured” [1].

The definition of data science is somewhat different from the definition of biomedical informatics, which is “the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health” [2].

Clearly there is overlap as well as complementarity. As noted by Payne et al., biomedical informatics deals with a broader spectrum of data and information tasks, focused not only on what is learned from data but also how that is applied in a broader sociotechnical context [2].

Many DMICE research programs focus on aspects of Data Science:
  • Re-use of data from EHR (William Hersh, Aaron Cohen, Steven Bedrick) – leveraging data in EHR to identify patients as candidates for research studies and signals for rare diseases (porphyria) [3]
  • Documenting genomic variation in leukemia (Shannon McWeeney) – allowing for repurposing of drugs [4]
  • Quality of data for clinical care and research (Nicole Weiskopf) – methods for insuring completeness and comprehensiveness of data for use in research, quality measurement, and other tasks [5]
  • Urinary microbiome in health and disease (Lisa Karstens) – identifying role of microbiome and how its genetics can be leveraged for diagnosis and treatment [6]
  • Use of ambient data to detect and manage clinician strain – Dana Womack, Paul Gorman [7]
DMICE educational programs include Data Science in many of their courses. Our Bioinformatics & Computational Biomedicine (BCB) major includes:
  • Data Harmonization and Standards for Translational Research - BMI 533/633 (Instructors: Melissa Haendel, Ph.D., Ted Laderas, Ph.D., Christina Zheng, Ph.D.)
  • Management and Processing of Large Scale Data –BMI 535/635 (Instructors: Michael Mooney, Ph.D., Christina Zheng, Ph.D.)
  • Computational Genetics -BMI 559/659 (Instructor: Shannon McWeeney, Ph.D.)
  • Bioinformatics Programming and Scripting - BMI 565/656 (Instructor: Michael Mooney, Ph.D.)
  • Network Science and Biology- BMI 567/667 (Instructor: Guanming Wu, Ph.D.)
  • Data Analytics –BMI 569/669 (Instructors: Brian Sikora, Delilah Moore, Ted Laderas, Ph.D.)
Our Health & Clinical Informatics (HCIN) major includes:
  • Introduction to Biomedical and Health Informatics - BMI 510/610 (Instructor: William Hersh, M.D.)
  • Analytics for Healthcare - BMI 524/624 (Instructors: Abhijit Pandit, M.B.A., Tracy Edinger, N.D.)
  • Clinical Research Informatics- BMI 523/623 (Instructor: Nicole Weiskopf, Ph.D., Robert Schuff)
We also have developed ample instructional materials in Data Science for other learners:
References

1. Donoho, D (2017). 50 years of Data Science. Journal of Computational and Graphical Statistics. 26: 745-766. https://dl.dropboxusercontent.com/u/23421017/50YearsDataScience.pdf.
2. Payne, PRO, Bernstam, EV, et al. (2018). Biomedical informatics meets data science: current state and future directions for interaction. JAMIA Open. 1: 136-141. https://academic.oup.com/jamiaopen/article/1/2/136/5068667.
3. Wu, S, Liu, S, et al. (2017). Intra-institutional EHR collections for patient-level information retrieval. Journal of the American Society for Information Science & Technology. 68: 2636-2648.
4. Tyner, JW, Tognon, CE, et al. (2018). Functional genomic landscape of acute myeloid leukaemia. Nature. 562: 526-531.
4. Weiskopf, NG, Bakken, S, et al. (2017). A data quality assessment guideline for electronic health record data reuse. eGEMS. 5(1): 14.
6. Karstens, L, Asquith, M, et al. (2016). Does the urinary microbiome play a role in urgency urinary incontinence and its severity? Frontiers in Cellular and Infection Microbiology. 6:78. https://doi.org/10.3389/fcimb.2016.00078.
7. Womack, D. (2018). Subtle cues: Qualitative elicitation of signs of strain in the hospital workplace. PhD Dissertation, Oregon Health & Science University.

Friday, March 8, 2019

Have We Passed the High-Water Mark of Seamless Technology?

Have we passed the high point of technology being seamless and easy to use? I recently had to give a presentation to faculty that included showing a few slides, and I assumed I could just bring my MacBook Pro, with its HDMI port, and plug it into the projector that was in the conference room on my campus where the meeting was taking place.

It was not meant to be. Despite having the right cables, my computer would not sync to the projector. That led to the search for other options. Which then led us into dongle hell. Maybe we should try the VGA adapter also available in the room. But of course my newer-model Mac had USB-C and not the older standard Mac connector that is still prevalent, at least among the dongles.

We finally got it all to work, although not without a lot of wasted time. The episode also got me thinking and wondering if we have passed with high-water mark of technology working seamlessly. Are those golden days overs?

I used to feel like an old-timer when I would reminisce about how technology used to be so awful in the late 20th century. I am sure I got snickers from the younger crowd when I bemoaned the old days of having to connect to my email over telephone modems, and trying to figure out the right number of commas in the ATDT command set of telephone modems to time when the lines would ask for things like long-distance codes to be entered. I also remember the days of bringing overhead transparencies of presentations just in case my computer would not sync with the archaic projectors. I recall the days before USB drives when transferring files between computers was sometimes impossible, especially with the disappearance of floppy disks (and presentation file sizes exceeding their capacity).

I believe that the golden days peaked shortly after the start of the 21st century. I would marvel that computers always now seemed to connect to and sync to projectors. First with wired Ethernet and then with the emergence of wifi, we were no longer at the mercy of telephone modems and noisy phone lines (or needing to find Internet cafes). We could connect to our email and institutional servers without make long-distance phone calls. We could even talk on the phone from almost anywhere via Skype.

But now we seem to be regressing. Newer projectors have more resolution, but don’t always work with older computers. Dongle hell is worst for the Mac, but the PC world is not immune. We have multiple connector types for projects, and different types of USB. An additional wrinkle is the need for encryption, and we can move files that pose very small risk (e.g., Powerpoint presentations) easily from where we create them to where we need to present them.

Technology still is marvelous when it works seamlessly. Hopefully the proliferation of cables, connectors, and security protocols will not make the golden era a distant memory

Thursday, February 14, 2019

Summary of New ONC and CMS Notices of Proposed Rulemaking for Health IT

This week, on the eve of the Healthcare Information Management Systems Society (HIMSS) conference, the Office of the National Coordinator for Health IT (ONC) and Centers for Medicare and Medicaid Services (CMS) each dropped a Notice of Proposed Rule Making (NPRM) concerning functionality, interoperability, certification, and others aspects of electronic health record (EHR) use.

These rules are required as described in the 21st Century Cures Act, which was major legislation to modernize aspects of biomedical research and health IT that passed with wide bipartisan majorities. I saw the act as a chance to "clean up" some of adverse and unintended effects of the Health Information Technology for Clinical and Economic Health (HITECH) Act (aka, meaningful use).

The Web page for the ONC NPRM not only has the 724-page proposed rule, but also a set of nine readable and understandable fact sheets about the rule and another seven devoted to aspects of the information blocking rule. A slide presentation from the HIMSS conference has a number of nice figures that diagram aspects of big picture. The fact sheets and slide presentation present a nice segue to then word-search in the 724-page PDF to find specific details.

Here is my summary of the key points of the ONC rule:
  • A new US Core Data for Interoperability (USCDI) that adds provenance information, 8 types of clinical notes, additional demographic information, and pediatric vital signs to the former Core Clinical Data Set
  • APIs to access USCDI using FHIR, with a base set of 13 Resources and two specific data fields within the Patient Resource that must be supported
  • SMART Application Launch Framework, using the OAuth2 and OpenID standards
  • Conditions and maintenance of EHR certification, with additional pediatric-specific criteria
  • Requirement to be able to export data for a patient and for all patients (when provider changing EHRs)
  • Rules for information blocking and allowable exceptions
  • Allowable fees to support information exchange (but not to the patient to access their data)
  • Standards Version Advancement Process to allow developers to choose among the versions of standards currently approved by National Coordinator
  • No developer gag clauses that prohibit discussion and demonstration of problems
The CMS NPRM is a companion rule that goes into more detail on some aspects, per its headings, some of which include:
  • Patient Access Through Application Programming Interfaces (APIs)
  • Health Information Exchange and Care Coordination Across Payers
  • API Access to Published Provider Directory Data
  • Care Coordination Through Trusted Exchange Networks
  • Public Reporting and Prevention of Information Blocking
  • Provider Digital Contact Information
  • Advancing Interoperability in Innovative Models
Clearly these rules are a necessary recalibration of our EHRs and their data, aiming to make them more patient-centric, clinician-friendly, and responsive to the EHR marketplace. While I suspect some smart people will come up with some good ideas why one thing or another should be changed in the final rules, my interpretation of the NPRMs is that they pretty much hit the target. I am confident they will lead to improved systems and our ability to do good with the data.