Wednesday, September 12, 2018

Artificial Intelligence in Medicine: 21st Century Resurgence

I first entered the informatics field in the late 1980s, at the tail end of the first era of artificial intelligence (AI) in medicine. Initial systems focused on making medical diagnoses using symbolic processing, which was appropriate for a time of relatively little digital data, both for individual patients and healthcare as whole, and underpowered hardware. Systems like MYCIN [1], INTERNIST-1/QMR [2], and DXPLAIN [3] provided relatively accurate diagnostic performance, but were slow and difficult to use. They also provided a single likely diagnosis, which was not really what clinicians needed. Because of these shortcomings, they never achieved significant real-world adoption, and their "Greek Oracle" style of approach was abandoned. [4]. There was also some early enthusiasm for neural networks around that time [5], although in retrospect those systems were hampered by lack of data and computing power.

Into the 1990s, informatics moved on to other areas, such as information retrieval (search) from the newly evolving World Wide Web and more focused (rule-based) decision support. At the start of the new century, I started to wonder whether I should still even cover those early AI systems in my well-known introductory informatics course. I kept them included, mainly out of a sense of historical perspective, since those systems were a major focus of work in the field in its early days. However, the term "AI" almost seemed to disappear from informatics jargon.

In recent years, however, AI in medicine (and beyond) has re-emerged. Driven by much larger quantities of data (through electronic health records, curated data sets - mainly images, and personal tracking devices) and much more powerful hardware (mainly networked clusters of low-cost computers and hard disks as well as mobile devices), there has been a resurgence of AI, although with a somewhat different focus from the original era. There has also been a maturing of machine learning techniques, most prominently neural networks applied in complex formats known as deep learning [6, 7].

The most success for use of deep learning has come in image processing. The well-known researcher and author Dr. Eric Topol keeps an ever-growing list of systems for diagnosis and their comparison with humans (to which I have contributed a few, and to which I add studies that have only been published as preprints on bioArXiv.org):
  • Radiology - diagnosis comparable to radiologists for pneumonia [8] tuberculosis [9], intracranial hemorrhage [10]
  • Dermatology - detecting skin cancer from images [11-13]
  • Ophthalmology - detecting diabetic retinopathy from fundal images [14-15], predicting cardiovascular risk factors from retinal fundus photographs [16]; diagnosis of congenital cataract [17], age-related macular degeneration [18], plus disease [19]; and diagnoses of retinal diseases [20] and macular diseases [21]
  • Pathology - classifying various forms of cancer from histopathology images [22-25], detecting lymph node metastases [26]
  • Cardiology - cardiac arrhythmia detection comparable to cardiologists [27] and classification of views in echocardiography [28]
  • Gastroenterology - endocytoscope images for diagnose-and-leave strategy for diminutive, nonneoplastic, rectosigmoid polyps [29]
Organized medicine has taken notice of AI. Journal of the American Medical Association recently published two perspective pieces [30, 31] as well as editorial [32] on how AI and machine learning will impact medical practice. I have heard anecdotally that some of the most heavily attended sessions at radiology meetings at those devoted to AI. I am sure there is a mixture of intellectual excitement tinged with some fear of future livelihood.

The success of these systems and the technology underlying them are exciting, but I also would tell any thoughtful radiologist (or pathologist, dermatologist, or ophthalmologist) not to fear for his or her livelihood. Yes these tools will change practice, maybe sooner than we realize. However, I always think that high-tech medicine of the future will look like how it is used the doctors of Star Trek. Yes, those physicians have immense technology at their disposal, not only for diagnosis but also for treatment. But those tools do not remove the human element of caring for people. Explaining to patients their disease process, describing the prognosis as we know it, and shared decision-making among the diagnostic and treatment options are all important in applying advanced technology is medicine.

I also recognize we have a ways to go before this technology truly changes medicine. For several years running, I have expressed both my intellectual excitement at predictive data science while also noting that prediction is not enough, and we must demonstrate that what is predicted must be demonstrated to be able to be applied to improve the delivery of care and patient health.

This notion is best elaborated by some discussion of another deep learning paper focused on a non-image domain, namely the prediction of in-hospital mortality, 30-day unplanned readmission, prolonged length of stay, and the entirety of a patient’s final diagnoses [33]. The paper demonstrates the value of deep learning, the application of Fast Healthcare Interoperability Resources (FHIR) for data points, and efforts for the neural network to explain itself along its processing path. I do not doubt the veracity of what the authors have accomplished. Clearly, deep learning techniques will play a significant role as described above. These methods scale with large quantities of data and will likely improve over time with even better algorithms and better data.

But taking off my computer science hat and replacing it with my informatics one, I have a couple of concerns. My first and major concern is whether this prediction can be turned into information that can improve patient outcomes. Just because we can predict mortality or prolonged length of stay, does that mean we can do anything about it? Second, while there is value to predicting across the entire population of patients, it would be interesting to focus in on patients we know are more likely to need closer attention. Can we focus in and intervene for those patients who matter?

Dr. Topol recently co-authored an accompanying editorial describing a study that adheres to the kind of methods that are truly needed to evaluate modern AI in clinical settings [34]. The study itself is to be commended; it actually tests an application of an AI system for detection of diabetic retinopathy in primary care settings [35]. The system worked effectively, though it was not flawless, and other issues common to real-world medicine emerged, such as some patients being non-imageable and others having different eye diseases. Nonetheless, I agree with Dr. Topol that this study sets the bar for how AI needs to be evaluated before its widespread adoption in routine clinical practice.

All of this AI in medicine research is impressive. But its advocates will need to continue the perhaps more mundane research of how we make this data actionable and actually act on it in ways that improve patient outcomes. I personally find that kind of research more interesting and exciting anyways.

References

1. Miller, RA (2010). A history of the INTERNIST-1 and Quick Medical Reference (QMR) computer-assisted diagnosis projects, with lessons learned. Yearbook of Medical Informatics. Stuttgart, Germany: 121-136.
2. Shortliffe, EH, Davis, R, et al. (1975). Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Computers and Biomedical Research. 8: 303-320.
3. Barnett, GO, Cimino, JJ, et al. (1987). DXplain: an evolving diagnostic decision-support system. Journal of the American Medical Association. 258: 67-74.
4. Miller, RA and Masarie, FE (1990). The demise of the "Greek Oracle" model for medical diagnostic systems. Methods of Information in Medicine. 29: 1-2.
5. Rumelhart, DE and McClelland, JL (1986). Parallel Distributed Processing: Foundations. Cambridge, MA, MIT Press.
6. Alpaydin, E (2016). Machine Learning: The New AI. Cambridge, MA, MIT Press.
7. Kelleher, JD and Tierney, B (2018). Data Science. Cambridge, MA, MIT Press.
8. Rajpurkar, P, Irvin, J, et al. (2017). CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv.org: arXiv:1711.05225. https://arxiv.org/abs/1711.05225.
9. Lakhani, P and Sundaram, B (2017). Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 284: 574-582.
10. Arbabshirani, MR, Fornwalt, BK, et al. (2018). Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine. 1: 9. https://www.nature.com/articles/s41746-017-0015-z.
11. Esteva, A, Kuprel, B, et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature. 542: 115-118.
12. Haenssle, HA, Fink, C, et al. (2018). Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of Oncology. 29: 1836-1842.
13. Han, SS, Kim, MS, et al. (2018). Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. Journal of Investigative Dermatology. 138: 1529-1538.
14. Gulshan, V, Peng, L, et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Journal of the American Medical Association. 316: 2402-2410.
15. Ting, DSW, Cheung, CYL, et al. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Journal of the American Medical Association. 318: 2211-2223.
16. Poplin, R, Varadarajan, AV, et al. (2017). Predicting cardiovascular risk factors from retinal fundus photographs using deep learning. arXiv.org: arXiv:1708.09843. https://arxiv.org/abs/1708.09843.
17. Long, E, Lin, H, et al. (2017). An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature Biomedical Engineering. 1: 0024. https://www.nature.com/articles/s41551-016-0024.
18. Burlina, PM, Joshi, N, et al. (2017). Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmology. 135: 1170-1176.
19. Brown, JM, Campbell, JP, et al. (2018). Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmology. 136: 803-810.
20. DeFauw, J, Ledsam, JR, et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine: Epub ahead of print. https://www.nature.com/articles/s41591-018-0107-6.
21. Kermany, DS, Goldbaum, M, et al. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 172: 1122-1131.E1129.
22. Bejnordi, BE, Zuidhof, G, et al. (2017). Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. Journal of Medical Imaging. 4(4): 044504. https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-4/issue-04/044504/Context-aware-stacked-convolutional-neural-networks-for-classification-of-breast/10.1117/1.JMI.4.4.044504.full?SSO=1.
23. Liu, Y, Gadepalli, K, et al. (2017). Detecting cancer metastases on gigapixel pathology images. arXiv.org: arXiv:1703.02442. https://arxiv.org/abs/1703.02442.
24. Yu, KH, Zhang, C, et al. (2017). Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nature Communications. 7: 12474. https://www.nature.com/articles/ncomms12474.
25. Capper, D, Jones, DTW, et al. (2018). DNA methylation-based classification of central nervous system tumours. Nature. 555: 469–474.
26. Bejnordi, BE, Veta, M, et al. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Journal of the American Medical Association. 318: 2199-2210.
27. Rajpurkar, P, Hannun, AY, et al. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv.org: arXiv:1707.01836. https://arxiv.org/abs/1707.01836.
28. Madani, A, Arnaout, R, et al. (2018). Fast and accurate view classification of echocardiograms using deep learning. npj Digital Medicine. 1: 6. https://www.nature.com/articles/s41746-017-0013-1.
29. Mori, Y, Kudo, SE, et al. (2018). Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Annals of Internal Medicine: Epub ahead of print.
30. Hinton, G (2018). Deep learning—a technology with the potential to transform health care. Journal of the American Medical Association: Epub ahead of print.
31. Naylor, CD (2018). On the prospects for a (deep) learning health care system. Journal of the American Medical Association: Epub ahead of print.
32. Stead, WW (2018). Clinical implications and challenges of artificial intelligence and deep learning. Journal of the American Medical Association: Epub ahead of print.
33. 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/s41746-018-0029-133.
34. Keane, PA and Topol, EJ (2018). With an eye to AI and autonomous diagnosis. npj Digital Medicine. 1: 40. https://www.nature.com/articles/s41746-018-0048-y.
35. Abràmoff, MD, Lavin, PT, et al. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine. 1: 39. https://www.nature.com/articles/s41746-018-0040-6.

Monday, July 30, 2018

Healthcare Information Technology Workforce: Updated Analysis Shows Continued Growth and Opportunity

A new analysis of the healthcare information technology (IT) workforce indicates that as hospitals and health systems continue to adopt electronic health records (EHRs) and other forms of IT, as many as 19,852 to 153,114 more full-time equivalent (FTE) personnel may be required [1]. The new study has been published by myself and colleagues Keith Boone and Annette Totten in the new journal, JAMIA Open. It updates an original analysis [2] from before the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act, which has led to substantial growth in the adoption of EHRs [3, 4] and this the expansion of the healthcare IT workforce.

The data used in the analysis actually focus only on hospitals and health systems, so informatics/IT workforce growth will also likely occur in other health-related areas. The results remind us that there remain and will likely be growing opportunities for those who train and work in biomedical and health informatics.

The new paper represents an update of a research interest of mine that emerged over a decade ago. As my activities in informatics education were growing at that time, I became interested in the characteristics of the healthcare IT workforce and its professional development. This led me to search for studies of that workforce, which essentially came up empty. There was a single resource I was able to find that provided some data about healthcare IT staffing, the HIMSS Analytics Database, but no one had ever done any analysis of it. The HIMSS Analytics Database mostly focuses on the IT systems that hospitals and health systems implement but also contains some data on IT staffing FTE. The result of the analysis was a paper that garnered a great detail of attention when it was published in 2008 [2], including an invitation to present the results in Washington, DC to the Capitol Hill Steering Committee on Telehealth and Healthcare Informatics.

Based on 2007 data, our initial paper looked at FTE staffing, especially as it related to level of adoption, based on the well-known HIMSS Analytics Electronic Medical Record Adoption Model (EMRAM), a 0-7 scale that measures milestones of EHR adoption. This was, of course, before the HITECH Act, when a much smaller number of hospitals and health systems had adopted EHRs. Also around that time, there had been the publication of the first systematic review of evidence supporting benefit of healthcare IT, showing the value came mainly from use of clinical decision support (CDS) and computerized provider order entry (CPOE) [5]. As such, we looked at the level of healthcare IT staffing by EMRAM stage, with a particular focus on what increase might be required to achieve the level of IT use associated with those evidence-based benefits. We assessed the ratio of IT FTE staff to hospital bed ratio by EMRAM stage.

Because the self-reported data of the database was incomplete for FTE staffing, we had to extrapolate from the data present to the entire country (recognizing a potential bias from those who responded vs. those who did not). We also noted some other limitations of the data, which was that the data represented only hospitals and health systems, and not the entire healthcare system, nor the use of IT outside of the healthcare system. Our analysis found that the national health IT workforce size in 2007 was estimated to be 108,390. But the real sound bite from the study was that if EHR adoption were to increase to the level supported by the evidence, namely EMRAM Stage 4 (use of CDS and CPOE), and FTE/Bed ratios remained the same for those hospitals, the size of the workforce would need to grow to 149,174. In other words, there was a need to increase the size of the healthcare IT workforce by 40,784 people.

Within a year of the study’s publication, the US economy was entering the Great Recession, and the new Obama Administration had taken office. The recession led to Congress passing the HITECH Act (as part of the American Recovery and Reinvestment Act), which allocated about $30 billion in economic stimulus funding to EHR adoption. Recognizing that a larger and better-trained workforce would be necessary to facilitate this EHR adoption, the HITECH Act included $118 million for workforce development. The rationale for this included the data from our study showing the need for expanding the workforce, especially as the meaningful use of EHRs required of HITECH would necessitate the use of CDS and CPOE.

Since that time, EHR adoption has grown substantially, to 96% of hospitals [3] and 87% of office-based physicians and other clinicians [4]. A few years ago, I started to wonder how the widespread adoption impacted the workforce, especially at the higher stages of EMRAM, which very few hospitals had achieved in 2007. By 2014, one-quarter of US hospitals had reached Stages 6 and 7.

The new study reports some interesting findings. First, the FTE/Bed ratios in 2014 for different levels of EMRAM are remarkably similar to those in 2007 (with the exception of Stage 7, which no hospitals had reached in 2007). However, because of the advancing of hospitals to higher EMRAM stages beyond Stage 4, the total workforce ended up being larger than we had estimated to be needed from the 2007 data. Probably most important, as more hospitals continue to reach Stages 6 and 7, the workforce will continue to grow. Our new study estimates that if all hospitals were to achieve Stage 6, an additional 19,852 healthcare IT FTE would be needed. Our analysis also shows an almost explosive growth of 153,114 more FTE if all hospitals moved to Stage 7, although we have less confidence in that result due to the relatively small numbers of hospitals that have achieved this stage at the present time., and it is also unclear whether the leaders reaching Stage 7 early are representative of the rest of hospitals and health systems generally.

Nonetheless, the US healthcare industry is moving toward increased EHR adoption. At the time of the data snapshot we used in the analysis in 2014, there were 3.7% and 22.2% of hospitals at Stages 6 and 7 respectively. The latest EMRAM data from the end of 2017 show those to have increased to 6.4% and 33.8% respectively. In other words, the healthcare industry is moving toward higher levels of adoption that, if our findings hold, will lead to increased healthcare IT hiring.

The new paper also reiterates the caveats of the HIMSS Analytics data. It is a valuable database, but not really designed to measure the workforce or its characteristics in great detail. Another limitation is that only about a third of organizations respond to the staffing FTE questions. In addition, while the hospital setting comprises a large proportion of those who work in the healthcare industry, there are other places where IT and informatics personnel work, including for vendors, research institutions, government, and other health-related entities. As healthcare changes, these latter settings may account for an even larger fraction of the healthcare IT workforce.

Because of these limitations of the data and the changing healthcare environment, the paper calls for additional research and other actions. We note that better data, both more complete and with more detail, is critical to learn more about the workforce. We also lament the decision of the US Bureau of Labor Statistics (BLS) to not add a Standard Occupational Classification (SOC) code for health informatics, which would have added informatics to US labor statistics. Fortunately the American Medical Informatics Association (AMIA) is undertaking a practice analysis of informatics work, so additional information about the workforce will be coming by the end of this year.

It should be noted that some may view the employment growth in healthcare IT as a negative, especially due to its added cost. However, the overall size of this workforce needs to be put in perspective, as it represents just a small fraction of the estimated 12 million Americans who work in the healthcare industry. As the need for data and information to improve operations and innovations in health-related industries grows, a large and well-trained workforce will continue to be critical to contribute toward the triple aim of improved health, improved care, and reduced cost [6]. In addition, and many career opportunities will continue to be available to those who want to join the informatics workforce.

References

1. Hersh, WR, Boone, KW, et al. (2018). Characteristics of the healthcare information technology workforce in the HITECH era: underestimated in size, still growing, and adapting to advanced uses. JAMIA Open. Epub ahead of print. https://doi.org/10.1093/jamiaopen/ooy029. (The data used in the analysis is also available for access at https://doi.org/10.5061/dryad.mv00464.)
2. Hersh, WR and Wright, A (2008). What workforce is needed to implement the health information technology agenda? An analysis from the HIMSS Analytics™ Database. AMIA Annual Symposium Proceedings, Washington, DC. American Medical Informatics Association. 303-307.  https://dmice.ohsu.edu/hersh/amia-08-workforce.pdf.
3. Henry, J, Pylypchuk, Y, et al. (2016). Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008-2015. Washington, DC, Department of Health and Human Services. http://dashboard.healthit.gov/evaluations/data-briefs/non-federal-acute-care-hospital-ehr-adoption-2008-2015.php.
4. Office of the National Coordinator for Health Information Technology. 'Office-based Physician Electronic Health Record Adoption,' Health IT Quick-Stat #50. http://dashboard.healthit.gov/quickstats/pages/physician-ehr-adoption-trends.php.
5. Chaudhry, B, Wang, J, et al. (2006). Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine. 144: 742-752.
6. Berwick, DM, Nolan, TW, et al. (2008). The triple aim: care, health, and cost. Health Affairs. 27: 759-769.

Saturday, June 23, 2018

Predatory Journals and Conferences Preying on a New Researcher

I had an interesting interaction from two parts of my life recently. One part emanated from my work as an informatics and information retrieval researcher, with interests that include the potential for the Internet to increasingly facilitate "open science," in which the methods, results, data, and generated knowledge are all more widely transparent and disseminated. An important part of open science is open-access publishing, an approach to publishing that changes its model to one where the research pays (usually through the grants that support their work) for the cost of publishing, and the resulting paper is made freely available on the Internet.

Unfortunately, there is a down side to open-access publishing, which is the proliferation of so-called predatory journals and conferences. These publications and events typically claim to be prestigious and offer peer review, but in reality they exist mainly to make money by trading on researchers’ need to publish and present their work [1, 2]. In reality, these venues have little if any peer review, as exemplified by cases of scientists submitting obviously bogus research that nonetheless is accepted for publication [3]. One Web site maintains a list of such journals. Another covers the topic exhaustively in the context of scientific fraud and misconduct.

This does not mean that all open-access journals have poor peer review (case in point are the Public Library of Science - PLoS and Biomedical Central - BMC journals). Nor does it mean that plenty of poor science does not make it through peer review in journals from traditional publishers. However, there is probably additional vigilance required when it comes to open-access journals. One cut point for journals I advise for students and others in biomedical sciences whom I mentor is it being included in the MEDLINE database, which has a threshold of peer review and other attributes that journals must reach to be listed (PLoS and BMC journals are in MEDLINE).

The part of my life that this story interacted with is the early research career of my daughter, an MD/MPH student at Oregon Health & Science University (OHSU), who recently had her first journal paper publication, on the heels of a number of poster abstract presentations [4]. Yes, I am a proud father!

But no sooner than her journal paper had been accepted and the ahead-of-print version posted, she started receiving the kinds of emails most of us in research receive on a daily basis, inviting submissions to predatory journals and presentations to similar conferences. She was excited to be invited to an international conference; I had to disappoint her to note the nature of such conferences (and that she would need to pay her way).

This episode shone a new light for me on the daily stream of nuisance emails from predatory journals and conferences that I receive. (One characteristic of these emails that helps me identify them as predatory is that they do not offer, as required under the US CAN-SPAM Act, an option to unsubscribe.) It annoys me that young researchers get exposed to this sort of thing at a more impressionable stage of their careers.

On a related note, now that she is published, my daughter’s next milestone will be to get her first citation, which will of course give her an h-index of 1. I had a tongue-in-cheek discussion with some of my geeky research colleagues as to if I cited her paper, whether it would be a form of scientific nepotism (a teachable moment about the h-index and citations!). However, I am certain she will receive many more legitimate citations as her career develops, so I will let her career grow without any intervention on my part, other than being a supportive parent.

References

1. Moher, D and Moher, E (2016). Stop predatory publishers now: act collaboratively. Annals of Internal Medicine. 164: 616-617.
2. Beall, J (2018). Predatory journals exploit structural weaknesses in scholarly publishing. 4Open. 1.
3. McCool, JH (2017). Opinion: Why I Published in a Predatory Journal. The Scientist.
4. Hersh, AR, Muñoz, LF, et al. (2018). Video compared to conversational contraceptive counseling during labor and maternity hospitalization in Colombia: a randomized trial. Contraception. Epub ahead of print.

Sunday, June 10, 2018

The EHR Strikes Back!

The last few years have been challenging for the electronic health record (EHR). While the Health Information Technology for Economic and Clinical Health (HITECH) Act succeeded in transitioning the US healthcare system mostly away from paper [1], the resulting electronic systems created a number of new problems [2]. They include diverting attention from patient care, adding to clinician time burdens, and causing outright burnout. Although the underlying problems of quality, safety, and cost of healthcare motivating the use of EHRs still exist, the large-scale adoption of EHRs has yet to solve them in any meaningful way.

I cannot imagine that many would advocate actually returning to paper medical records and fax-based communications. But clearly the new problems introduced by EHRs must be addressed while not losing sight of the original motivations for them. Fortunately, a more nuanced view of the EHR is emerging, and based on some recent happenings I will describe next, it may be said that the EHR is striking back.

The first strike back was an "Ideas and Opinion" piece in the medical journal, Annals of Internal Medicine. Presenting data on note length in the EHR gathered by use of the Epic EHR in different countries, Downing et al. found that the length of notes in the US was substantially longer than those in other countries [3]. The authors contend that this is due the priority of EHR use in the US for billing and other non-direct aspects of clinical care. They suggest that these uses beyond the direct clinical encounter, and not the EHR itself, are the cause for physician dissatisfaction and burnout.

A second strike back is the release of a Harris poll at a Stanford symposium to re-imagine the EHR and make it more useful for physicians. The poll of over 500 primary-care physicians (PCPs) on the EHR showed that these physicians saw value in the EHR but that they also desired substantial improvements.

About two-thirds of these physicians agreed with the statement that EHRs have led to improvement in care (63%) and were somewhat or more satisfied with their current systems (66%). But significant numbers of these PCPs also acknowledged problems:
  • 40% said there are more challenges than benefits with the EHR
  • 49% believed that using an EHR detracted from their clinical effectiveness 
  • 71% stated that EHRs greatly contribute to physician burnout 
  • 59% agreed that EHRs need a complete overhaul
The surveyed PCPs did not see EHRs as powerful clinical tools, but rather found their primary value in data storage (44%), with only 8% agreeing their primary value was clinically related.

Their survey also found substantial agreement on what should be fixed immediately versus in the longer term:
  • 72% believed that improving the EHR user interfaces could best address EHR challenges in the immediate future 
  • 67% agreed that solving interoperability deficiencies should be the top priority for EHRs in the next decade
  • 43% desired improved predictive analytics to support disease diagnosis, prevention, and population health management
A final strike back for the EHR is the announcement by Apple of their new Healthkit, a collection of application programming interfaces (APIs) that add value to the functionality recently added to the Apple Health app that allows people to download their data via the Fast Healthcare Interoperability Resources (FHIR) standard. These APIs will allow new apps to be developed that implement ways to use their data to improve their health and healthcare. Apple has historically been a company that has tended to isolate its ecosystem and has been slow to adopt standards, but this move into the most important data standard for healthcare is to be lauded. The present functionality of being able to download data into the Apple Health app is limited, but the functionality of downloading it with FHIR and providing an API for use of the data opens to door to many applications, some of which no one has even conceptualized.

The EHR has certainly taken it on the chin of late, deservedly so. But with the foundation that has been laid by HITECH, recognition of the problems being more related to the healthcare system than the EHR per se, and new innovations such as those from Apple and others who devise new methods to do interesting things with the data, we will hopefully find new innovations that address problems in healthcare and enable new applications that improve personal and public health.

References
1. Washington, V, DeSalvo, K, et al. (2017). The HITECH era and the path forward. New England Journal of Medicine. 377: 904-906.
2. Halamka, JD and Tripathi, M (2017). The HITECH era in retrospect. New England Journal of Medicine. 377: 907-909.
3. Downing, NL, Bates, DW, et al. (2018). Physician burnout in the electronic health record era: are we ignoring the real cause? Annals of Internal Medicine. Epub ahead of print.

Thursday, June 7, 2018

New Edition of Textbook, Health Informatics: Practical Guide

I am pleased to announce that I am Co-Editor of the newly published, Health Informatics: Practical Guide, Seventh Edition. The original editor, Robert Hoyt, MD, asked me to come on as Co-Editor for this edition. I will assume sole editorship starting with the Eighth Edition. Although Bob and his wife Ann Yoshihashi deserve credit for the lion’s share of the painstaking details that books like this require, I am pleased to note that I was also involved in the authorship of eight of the book’s 22 chapters.

Bob and Ann have always used an interesting approach to publishing that has arisen in the Internet era, which is so-called self-publishing. They have used the site Lulu.com, which features print-on-demand as well as electronic versions. Although I mostly prefer electronic books these days, the first picture below shows the smiling Co-Editor with his first paper copy. The second picture below shows the back cover that lists the table of contents of the book.



The book is available for purchase on the Lulu.com Web site in both print and eBook PDF formats. The book will also be made available from the more “traditional” online booksellers, such as Amazon.com. Bob also maintains a Web site for the book that includes a special area for those who use the book as instructors (and can register for a free evaluation copy).

The content of the new book is well-aligned with the well-known introductory biomedical and health informatics course that I teach, which is variably called 10x10 (“ten by ten,” the standalone version) and BMI 510 (one of the initial courses in our graduate program).

The chapters I authored or co-authored include:
  • 1) Hoyt, RE, Bernstam, EV, Hersh, WR, Overview of Health Informatics
  • 3) Hersh, WR, Hoyt, RE, Computer and Network Architectures
  • 5) Hersh, WR, Standards and Interoperability
  • 6) Hoyt, RE, Hersh, WR, Health Information Exchange
  • 7) Hersh, WR, Healthcare Data Analytics
  • 12) Hersh, WR, Gibbons, MC, Shaihk, Y, Hoyt, RE, Consumer Health Informatics
  • 14) Hoyt, RE, Hersh, WR, Evidence-Based Medicine and Clinical Practice Guidelines
  • 15) Hersh, WR, Information Retrieval from Medical Knowledge Resources
I look forward to getting feedback on the book and suggestions for improvement, especially for the next edition.

Tuesday, May 15, 2018

Kudos for the Informatics Professor - 2018 Update

It has been a while since I have posted one of my periodic kudos for the Informatics Professor, so let me take the opportunity to do so for late 2017 and early 2018.

A blog posting of mine received some unexpected attention. As I always do when responding to a government Request for Information (RFI), I posted comments in my blog that I submitted to the RFI for the NIH draft Data Science plan. My main point was that while the plan was a good start, it needed to have more to achieve the optimal value for data science related to health and research. First, the blog posting was picked up by Politico (about a third of the way down the page). I was then asked by National Library of Medicine (NLM) Director Patricia Brennan to re-write it as a guest posting to the NLM Director’s Blog.

Last month, I took part in the inaugural meeting of the International Academy for Health Sciences Informatics (IAHSI), a new Academy of 121 elected members who are leaders in informatics from around the world. With about 50 others from the Academy, I took part in a day-long meeting that was co-located with Medical Informatics Europe 2018 in Gothenburg, Sweden.


I am also honored to be invited to serve on the Scientific Advisory Board (SAB) of the 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 the next meeting of the SAB in Cape Town, South Africa in July. I have been asked to contribute based on my expertise in clinical informatics.

I also gave some invited international talks, including:
Back in the US, the AMIA Clinical Informatics Fellows (ACIF) group has been publishing a series of podcasts. I was delighted to be interviewed for one of them.

Finally, I have authored a chapter in a newly published book: Rydell RL and Landa HM (eds.), The CMIO Survival Guide: A Handbook for Chief Medical Information Officers and Those Who Hire Them, 2nd Edition, CRC Press, 2018. My chapter is entitled, Education and Professional Development for the CMIO. (Surprise!)

Monday, May 7, 2018

Access to Health IT and Data Science Curricular Resources

Over the last decade, I have had the fortunate opportunity to be involved in two efforts to develop widely available curricular resources in health information technology and data science. While these resources are a great foundation for others to use to develop courses and other content in this area, the fact that they were developed with federal grants whose funding has now ended means that they will no longer be updated at their source. They will fortunately continue to be freely available on Web sites, but further development, at least from the source, will not occur for now.

Some might ask, why can’t you update the materials? Updating would be feasible if the materials were just simple textual resources or slides. But these materials contain much more, including narrated lectures, transcripts of those lectures, and packaging that makes them flexible to use. And even if we did just aim to simply update the content, I know from other teaching I do that it takes time and effort, not only the time of content authors, but also of instructional designers, technical support staff, and others who create useful products and packaging.

Nonetheless, the materials themselves will continue to be available, and I will use the rest of this posting to describe what material is available, some history on its development, and where the most recent versions can be found.

The Office of the National Coordinator for Health IT (ONC) Curriculum was developed initially under funding from the Health Information Technology for Economic and Clinical Health (HITECH) Act. Recognizing that adoption and meaningful use of electronic health records (EHRs) would require training a workforce to implement them, the ONC funded a workforce development program that included not only this curriculum development, but also funding for training in both community colleges and universities. The final version of the initial curriculum, completed in 2013, was posted on the Web site of the American Medical Informatics Association (AMIA).

In 2015, the ONC found additional funding to update the curriculum and add some new content around health IT and value-based care. The funding also included the development of short training courses, such as the Healthcare Data Analytics course that we have since developed into a standalone course that offers continuing education credit for physicians and nurses. The final curriculum itself is now available for downloading from the ONC Web site.

It should be noted that while these materials are freely available to anyone, the audience for them is focused on educators. The curriculum consists, in ONC jargon, of components, each comparable in quantity to a college-level course. In other words, the curriculum is an extensive resource that can be enhanced by those who develop and maintain courses. Self-directed learners can certainly make use of the materials, and are not discouraged from doing so, but their volume and breadth would make it challenging to design an appropriate course of study. But an experienced education should be able to adapt them appropriately.

The second resource that was developed, but for which funding has ended as well, is the OHSU Big Data to Knowledge (BD2K) Open Educational Resources (OERs) Project. The development of these materials was funded under a grant from the National Institutes of Health (NIH) BD2K Program. Like the ONC curriculum, these materials are freely available for others to use and enhance. They can be viewed on the project Web site or downloaded as source materials from a GitHub repository. While they are not quite as exhaustive as the ONC components, these modules are more manageable for self-directed learners. The Web site for these materials provides a number of examples of their use, including their being mapped to the biomedical informatics competencies of the NIH Clinical And Translational Science Awards (CTSA) Program.

One limitation to both sets of these materials is that they are not able to incorporate any copyrighted material from any other source. While those of us who teach in universities that subscribe to journals and other resources are able to use portions of such content, password-protected in learning management systems, under fair use rules, putting copyrighted material out in the public domain is not allowed. This is another role of the educator or other content expert, to make appropriate use of copyrighted matter.

The components of the ONC Health IT Curriculum consist of the following:
  1. Introduction to Health Care and Public Health in the U.S. 
  2. The Culture of Health Care 
  3. Terminology in Health Care and Public Health Settings 
  4. Introduction to Information and Computer Science 
  5. History of Health Information Technology in the U.S. 
  6. Health Management Information System 
  7. Working with Health IT Systems 
  8. Installation and Maintenance of Health IT Systems 
  9. Networking and Health Information Exchange 
  10. Health Care Workflow Process Improvement 
  11. Configuring EHRs 
  12. Quality Improvement 
  13. Public Health IT 
  14. Special Topics Course on Vendor-Specific Systems 
  15. Usability and Human Factors 
  16. Professionalism/Customer Service in the Health Environment 
  17. Working in Teams 
  18. Planning, Management and Leadership for Health IT 
  19. Introduction to Project Management 
  20. Training and Instructional Design 
  21. Population Health 
  22. Care Coordination and Interoperable Health IT Systems 
  23. Value-Based Care 
  24. Health Care Data Analytics 
  25. Patient-Centered Care 
The modules of the BD2K OERs materials consist of the following:
  • BDK01 Biomedical Big Data Science 
  • BDK02 Introduction To Big Data In Biology And Medicine 
  • BDK03 Ethical Issues In Use Of Big Data 
  • BDK04 Clinical Data Standards Related To Big Data 
  • BDK05 Basic Research Data Standards 
  • BDK06 Public Health And Big Data 
  • BDK07 Team Science 
  • BDK08 Secondary Use (Reuse) Of Clinical Data 
  • BDK09 Publication And Peer Review 
  • BDK10 Information Retrieval 
  • BDK11 Identifiers 
  • BDK12 Data Annotation And Curation 
  • BDK13 Learn FHIR 
  • BDK14 Ontologies 101 
  • BDK15 Data Metadata And Provenance 
  • BDK16 Semantic Data Interoperability 
  • BDK17 Choice Of Algorithms And Algorithm Dynamics 
  • BDK18 Visualization And Interpretation 
  • BDK19 Replication, Validation And The Spectrum Of Reproducibility 
  • BDK20 Regulatory Issues In Big Data For Genomics And Health 
  • BDK21 Hosting Data Dissemination And Data Stewardship Workshops 
  • BDK22 Guidelines For Reporting, Publications, And Data Sharing

Wednesday, April 11, 2018

Marching Again in the March for Science

Last year I gave some thought before deciding to participate in the Portland March for Science. It was not that I am afraid to express my political views, but rather I had some hesitation about politicizing science. In the end, however, I felt compelled to take a stand against what I view as attacks on science by those whose political views with which I also happen to disagree. I was also afraid for science last year, as the new Administration was threatening huge cuts threatened for its funding.

I have no hesitation in deciding to participate again this year. I actually find myself less alarmed about the impact of the current political environment on science than I was a year ago. While some areas of science (e.g., climate change) are a good deal more impacted than those of us in the biomedical and informational sciences, the federal budget for science this year reflects the usual bipartisan support, at least the latter areas. Even though I do have concerns for those want to slash the budget of the Agency for Healthcare Research and Quality (AHRQ), the National Institutes of Health (NIH) has fared quite well. There is such value for federally funded science research, not only for the basic discoveries that lead to improved health and delivery of healthcare, but it also boost the local economies of organizations that successfully compete for grants and other funding. It even has a multiplier effect, as scientific research leads to local hiring, and those who are hired then spend money at local grocery stores, eating establishments, and other businesses.

Both last year and this year, I have been impressed that a number of Republicans, whose policy views in general I probably mostly disagree with, have been outspoken on the importance of funding biomedical research. It was fascinating for me last year, when Republican Senator Roy Blunt (R-MO) said, A cut to NIH is not a cut to Washington bureaucracy — it is a cut to life-saving treatments and cures, affecting research performed all across the country.

I also enjoyed the camaraderie as well as the funny signs last year and presume I will this year. I appreciate the organizers call for the march to be pro-science and not anti-anything. I hope the turnout will be strong and positive.

Sunday, April 1, 2018

Response to NIH RFI for Input on Draft Strategic Plan for Data Science

The National Institutes of Health (NIH), the premiere biomedical research organization in the US (and the world), has issued a Request for Information (RFI) that solicits input for their draft Strategic Plan for Data Science. As I did with the request for public input to the now-published Strategic Plan for the National Library of Medicine (NLM), I am posting my comments in this blog as well as submitting them through the formal collection process. I also made a similar posting with my comments on the NLM's RFI for promising directions and opportunities for next-generation data science challenges in health and biomedicine.

The draft NIH data science plan is a well-motivated and well-written overview of what NIH should be doing to insure that the value of data science is leveraged to maximize its benefit to biomedical research and human health. The goals of connecting all NIH and other relevant data, modernizing the ecosystem, developing tools and the workforce skills to use it, and making is sustainable are all important and articulated well in the draft plan.

However, there are three additional aspects that are critical to achieving the value of data science in biomedical research that are inadequately addressed in the draft. The first of these is the establishment of a research agenda around data science itself. We still do not understand all the best practices and other nuances around the optimal use of data science in biomedical research and human health. There are questions of how we best standardize data for use and re-use. What are the standards needed for best use of data? What are the gaps in current standards that can improve them to improve use of data in biomedical research, especially data that is not originally collected for research purposes, such as clinical data from the electronic health record and patient data from wearables, sensors, or that is directly entered?

There also must be further research into the human factors around data use. How do we best organize workflows for optimal input, extraction, and utilization of data? What are the best human-computer interfaces for such work? How do we balance personal privacy and security versus the public good of learning from such data? What are the ethical issues that must be addressed?

The second inadequately addressed aspect concerns the workforce for data science. While the draft properly notes the critical need to train specialists in data science, there is no explicit mention of the discipline that has been at the forefront of “data science” before the term came into widespread use. This is the field of biomedical informatics, whose education and training programs have been training a wide spectrum of those who work in data science, from the specialists who carry out the direct work as well as the applied professionals who work with researchers, the public, and others who implement the work of the specialists. NIH should acknowledge and leverage the wide spectrum of the workforce that will analyze and apply the results of data science work. The large number of biomedical (and related flavors of) informatics programs should expand their established role in translating data science from research to practice.

The final underspecified aspect concerns the organizational home for data science within NIH. The most logical home would be the National Library of Medicine (NLM), which is the new home of the Big Data to Knowledge (BD2K) program that was launched by NIH several years ago. The newly released NLM strategic plan is a logical complement to this plan. (Ideally, the NLM should be more appropriately named the National Institute for Biomedical Informatics and Data Science - NIBIDS - with the  Library function being one of its critical functions.)

With the addition of these concerns, the NIH data science plan can make an important contribution to realizing the potential for data science in improving human health as well as preventing and treating disease.

Monday, March 19, 2018

Physician Training in Clinical Informatics: One Size Does Not Fit All

Readers of this blog know that although I believe that formal recognition of physicians through board certification is great for our field and those who work in it, its implementation as a subspecialty and the requirement of formal ACGME-accredited fellowships as the only pathway to certification are detriments.

Two recent events bear this out. One recent happening is the increasing number of OHSU medical students who seek informatics training during medical school, such as a combined MD/MS program similar to the joint MD/MPH degree that many medical schools offer. The other is a publication of a supplement on the topic of the value of competency-based, time-variable education in the premier journal of medical education, Academic Medicine.

In essence, is a two-year, on-the-ground fellowship the only way to prepare physicians for practice in clinical informatics? As one who has been involved in the training of physicians for careers in informatics by diverse pathways, I take exception. After being halfway through the third year of our ACGME-accredited fellowship at OHSU, I certainly believe it is probably the gold standard for clinical informatics training. Yet it is not clear to me it is the only way, especially for the substantial number of physicians who come to informatics long after they completed their primary medical training and complete one of our graduate degree programs. Or even those who obtain such education during their primary training, such as the students in our MD/PhD program or those who may choose to pursue an MD/MS pathway.

Some question whether I am opposed to rigor in informatics training? Indeed I am not, but I believe there are many approaches to rigor in informatics training. A two-year, time-based fellowship is not the only path to rigorous training in the field.

My preference would be for there to be many pathways to formal clinical informatics training, all with appropriate rigor. All of them should include both substantial coursework to gain the requisite knowledge of the field, and the appropriate hands-on in-the-trenches training to experience the “real world.” Medical training is increasingly abandoning the “time on the ground” model; should not informatics too? I can easily envision a multifaceted path to informatics training where there is an appropriate amount of knowledge-based education (e.g., master’s degree in medical school or mid-career) followed by an appropriate amount of project work (either within the master’s or external to it).

Thursday, February 22, 2018

Next Frontier for Informatics Education: College Undergraduates

In the upcoming spring academic quarter (April-June, 2018), some faculty and I from our Department of Medical Informatics & Clinical Epidemiology (DMICE) will be pursuing a new frontier of informatics teaching, launching an introductory health informatics course for college undergraduates. The course will be offered in the new joint Oregon Health & Science University (OHSU)-Portland State University (PSU) School of Public Health (SPH). The new school merged previous academic units in public health from OHSU and health studies programs at PSU.

Our goals for the course are to introduce informatics skills and knowledge to undergraduate health-related majors as well as raise awareness about careers and graduate study in biomedical and health informatics.

As noted in the course syllabus, the learning objectives for the course include:
  • Introduce students to problems and challenges that health informatics addresses
  • Introduce students to the research and practice of health informatics
  • Provide all students with basic skills and knowledge in health informatics to apply in their future health-related careers
  • Lead students in discussion around ethical and diversity issues in health informatics
  • Provide additional direction to those interested in further (i.e., graduate) study in the field
The course will cover the following topics:
  1. Overview of Field and Problems That Motivate It
  2. Health Data, Information, and Knowledge
  3. Electronic Health Records
  4. Personal Health Records and Decision Aids
  5. Information Retrieval (Search)
  6. Bioinformatics
  7. Informatics Applications in Public Health
  8. Data Science, Analytics, and Visualization
  9. Ethical Issues in Health Informatics
  10. Careers in Health Informatics
Readers of this blog will likely hear more about this course in the near future!

Wednesday, February 7, 2018

The Three Parts of My Job: What I Love, Like, and Dislike

I am very thankful in life to have a career that is both enjoyable and rewarding. Years ago, a head hunter recruiting me for a different position asked what my ideal job would be. I paused for only a second or two, and then stated that my current job was my ideal job. It still is. I do not necessarily enjoy every minute of every day, but as I often tell people, I enjoy going to work most days, which is a pretty good indicator of how much one likes their job.

At other times, I tell people that I can break the activities of my job into three categories, which are (a) activities I enjoy and find deeply satisfying, (b) activities that I like that also enable things in the first category, and (c) things I truly dislike.

Most of the parts of my job I truly enjoy involve either my intellectual work in the biomedical and health informatics field or interactions with students and colleagues. Certainly among the major things I love revolve around teaching. I believe I am particularly skilled at taking the complexity of the informatics field; distilling out the big picture, including why it is important; and conveying it through writing, speaking, and other venues. I also enjoy teaching because it requires me to keep up to date with the field. I enjoy constantly learning myself, especially as new areas of the field emerge.

I also enjoy my interactions with people, especially students. I sometimes half-joke that my interactions with learners provides me a similar kind of satisfaction that I no longer get since I gave up practicing medicine a decade and a half ago. One really nice aspect of mentoring learners is that they come in all ages and experiences. I am no longer very young, but some of the people I teach are older than me. I also enjoy mentoring others, including those who have completed their education and are advancing in the field. This especially includes young informatics faculty, both at my university and at others.

Another enjoyable aspect of my job is disseminating knowledge in diverse ways. I have found the Internet as a platform and educational technology as a vehicle to share my knowledge. As noted in a previous post, I also enjoy the opportunity to travel around the world and see informatics play out in other cultures and economies.

The second category of my work consists of activities that I like, or at least do not find onerous. Many of these activities enable my being able to do those in the first category. These include many of my administrative duties as Chair of my department. Fortunately my leadership role in my department is nowhere near a full-time job, which means that I am still able to spend plenty of time on the activities in the first category above.

Finally, there are some aspects of my job that I dislike. Most of these revolve around less-than-pleasant interactions with people with whom I work. One thing I particularly do not enjoy is managing conflicts among those who report to me. I also do not enjoy managing those who do not meet reasonable expectations for their work. And of course there is no fun when budgetary problems arise.

I sometimes think back to a conversation I had a couple years ago with the now-retired President of our university, who was previously the Dean of the School of Medicine. He lamented that one down side to reaching his level was that he did not get to work in his field (ophthalmology) any more. This really struck me, and made me realize that informatics is what makes me work life interesting, and I could never see completely giving up the intellectual side of the field.

Tuesday, February 6, 2018

Business Travel and Airline Loyalty

One aspect of my work I enjoy is travel, both to domestic and international destinations. I enjoy the opportunity to attend scientific conferences and disseminate my own knowledge by giving talks and teaching. I also enjoy seeing how informatics plays out in the rest of the world, and how its issues good and bad are universal around the globe. Beyond informatics, I just enjoy visiting different parts of the United States and the rest of the world.

Part of my enjoyment stems from my fascination with airplanes. While I can’t say that I enjoy every aspect of air travel, especially delays and cancellations, I do enjoy arriving at my destinations and airplane spotting and tracking along the way. I have fun both tracking down new or unusual types of planes or different liveries that I do not frequently get to see. My photo archive is filled with such photos, and sometimes my biggest photography disappointments are when I am unable to get good pictures of such planes. I have gone out of my way to get booked on new planes. Not only can I remember my first rides on the Boeing 777, the Airbus 380, and the Boeing 787 Dreamliner, I am currently anticipating my first ride on an Airbus 350.

I also take my airplane travel very seriously, due in part to how much time I spend on airplanes and knowing that good seats and other amenities truly matter. My frequent travel puts me into the category of “business traveler.” This type of traveler does not seek the absolute lowest fare. While the fare difference cannot be exorbitant, factors of convenience and comfort also play into the equation. In addition, every business travel also knows that there is great value to loyalty to one airline, usually whereby one can achieve “status.”

I learned about airline loyalty initially on American Airlines. But American never really had a strong presence at Portland International Airport (PDX), so about two decades ago, I switched my loyalty to United Airlines. United historically had a strong presence at PDX, with flights not only to its major hubs but also up and down the west coast. I also valued its integration in the Star Alliance, not to mention the ability to have status in almost any airport in the world.

But in recent years, United had been reducing its presence in Portland (and actually the entire Pacific Northwest). I found myself sometimes having connections with long layovers or prices that were simply not tenable, even if they did not need to be the absolute cheapest. United essentially now only flies from PDX to its major hubs in Denver, Chicago, Houston, San Francisco, and Washington, DC.

So last year, I decided to give Alaska Airlines a try. Their West coast coverage is comprehensive, and they increasingly fly to many cities around the country, if not from Portland then from their primary hub in Seattle. They had a robust partnership with Delta Airlines, which was ideal for PDX, since Delta is the only airline to fly non-stop from PDX to Asia and Europe. Alaska also has a partnership with American, including sharing of lounges, as well as a number of global partners.

But also this past year, Alaska dialed down those partnerships. They ended their relationship with Delta and dialed back with American in order for the federal government to allow their acquisition of Virgin America. One can no longer get elite-qualifying miles on all American flights, but only those that are code share flights, mostly from American hubs to places Alaska does not fly. Alaska passengers no longer get elite benefits on American, which is unfortunate since early boarding and better seats not only allow better productivity on the plane, but also insure I will find overhead bin space for the suitcase I usually carry on and would prefer not to have to check (and potentially not arrive at my destination).

This year, I decided to return to United. It is true that Alaska has growing numbers of destinations. It is unbeatable for western destinations, especially those in California, Arizona, and Hawaii. Alaska flies non-stop to my two most common destinations where United flies, Chicago and Washington, DC, but United actually has more non-stop flights to Chicago and plenty of options for all DC airports.

Alaska has a growing number of non-stop flights to other Eastern destinations, although more typically, one needs to connect to less common cities through an American hub. Some of the Alaska Eastern destinations are only available on red-eye flights. As a business traveler, I have no interest in red-eye flights, which not only make me tired the next day, but also arrive at the wrong time (early instead of late in the day, so one can’t check into a hotel right away). Even though United has fewer flights out of PDX, I can get to almost anywhere on those flights by flying through one of their hubs. I will occasionally have missed connections, but Alaska has delayed and cancelled flights too, and I once got stuck in Seattle due to a freak snowstorm, with the pilots timing out before we could take off for the 37-minute flight to Portland. (My rebooked flights were canceled too, so I ended up renting a car and driving to Portland.)

The Alaska lounge situation is also sub-optimal. Their lounge at PDX is substandard, and while Alaska reciprocates with American Admiral’s Clubs in many cities, some of those lounges are in different terminals from the Alaska gates (e.g., Boston and San Diego), meaning it is almost impossible to have enough time to go through security to visit the lounge and then back out and through security again to get to the Alaska gates.

Alaska has a decent situation with its global partners, but it is just not the same as the Star Alliance, where status on your home airline (United, in my case) gets you status with all of the airlines in the alliance (especially Lufthansa and ANA).

United also has better technology. Its mobile app is more functional, and while United was late to the game with wifi, its satellite-based wifi is superior to the terrestrial GoGo.

In returning to United, I will have to live with some of the disadvantages - requiring connecting flights except to hubs and having to live with occasional disruptions due to missed connections. But United management seems to have improved, and its prices are more competitive out of PDX for now. This makes the calculus for me, as a business traveler, to have United as my preferred airline for travel.

Monday, January 22, 2018

2018 Update of Site, What is Biomedical & Health Informatics?

All through my career, I have been asked on a regular basis, What is Medical/Biomedical/Health Informatics? Years ago, to answer this question, I created a Web site that provided my to answer it. Over time, I added some voice-over-Powerpoint lectures, which also provided me the opportunity to demonstrate the technologies we use in our distance learning program at Oregon Health & Science University (OHSU) as well as the 10x10 ("ten by ten") course that I teach in partnership with the American Medical Informatics Association (AMIA).

I strive to keep the site up to date and am pleased to announce that I have now updated the lectures, references, and other content on the site. I look forward to receiving feedback from people and take full responsibility for any errors in any of the materials I have produced.

The educational methods I use on this site mirror my on-line teaching. I have always found great value in voice-over-Powerpoint lectures, especially using the Articulate Presenter tool that provides the slides and sound in Flash and HTML5 format and also allows easy navigation among the slides. I also provide PDF files of the slides as well as another PDF that has references to all of the papers, reports, books, and other citations in the lecture. The site also contains a list of key textbooks as well as links to some of my papers and to important organizations and other sites for the field.

Saturday, December 30, 2017

Annual Reflections at the End of 2017

As longtime readers of this blog know, I always end each year with an annual reflection on the year past. I did this in the first year of the blog of 2009, and have done it every year since.

The life of this blog has seen a remarkable transformation of the biomedical and health informatics field, especially for those of us who had been working in it for a long time. In my case, I entered the field in 1987 when I started my NLM postdoctoral fellowship at Brigham & Women’s Hospital in Boston, MA. After spending three years in Boston, I arrived at Oregon Health & Science University (OHSU) as a newly minted Assistant Professor. I have climbed the ranks to full Professor and am the inaugural (and still only ever) Chair of the School of Medicine’s Department of Medical Informatics & Clinical Epidemiology.

During my career, I have witnessed a great deal of other change and growth in information technology. I witnessed the birth of the World Wide Web in the early 1990s (with skepticism it could really work since the bandwidth of the Internet was so slow back then). I was doing information retrieval (search) before the emergence of Google (why didn’t I come up with the idea of ranking Web page output by number of links to each page?). And I watched the rest of healthcare, especially the policy folks, “discover” the potential benefits of the electronic health record (EHR).

It could be argued that EHRs were not quite ready for prime time when new President Barack Obama unveiled the Health Information Technology for Clinical & Economic Health (HITECH) Act, with the American Recovery & Reinvestment Act (ARRA). HITECH can certainly be criticized in hindsight that the “meaningful use” program had too much emphasis on process measures and not enough on information exchange or standards and interoperability. But, as those of us glass-half-full types would note, we do have a wired healthcare system now, and the next challenge is to meet the needs of patients and their providers.

I always remember students who asked, in the early days of HITECH, whether there would be jobs once we were “done” implementing. Of course, not only is implementation of large and complex software systems never truly “done,” there is so much more to do to obtain value.

As for me personally, I still remain gratified by my career choice at the intersection of medicine and computers. My interactions with my colleagues and my students, helping and mentoring them in different ways, gives me that nice human touch that I abandoned through making the decision in 2001 to stop seeing patients.

My department at OHSU continues to thrive under my leadership and, more importantly, the dedication of faculty, staff, and students. Our research programs are still being impactful and well-funded, and the enrollment in our various educational programs remains strong.

My family also adds a critical dimension to my life, with the academic and career successes of my wife and two daughters as gratifying as my own. I did suffer a couple unfortunate losses this year, with the passing of both my mother and father. Fortunately both lived long relatively healthy lives, although my mother’s last years were compromised by dementia. I do miss them both, and am sad that they will not see the rest of my family and I going forward in life.

And of course this blog is doing well. Last year I touted reaching 400,000 page views. This past month I barreled through the half-million page views milestone, and was able to make the 500,000th view myself, as seen in the picture below.


There are still challenges ahead, both for myself and the field. But this year, and likely next year, I receive comfort not only from family, friends, and colleagues, but also the satisfaction of my work.

Thursday, December 21, 2017

Apple Watch 2, A Year On

About a year ago, I described my early experience with the Apple Watch 2. I noted that based on my priorities for a digital watch, the Apple Watch 2 had excellent hardware but some limitations with its software. A year later, the software has improved, but is still not as good as I might like.

Everyone has different needs for devices such as a digital watch, and mine mostly revolve around running. Other functions, such as telling time, viewing local weather, and accessing text messages, are secondary. My main needs for running center around access to the data. I need data from my runs to live in the cloud, and not be stuck on my phone. I want to be able to access the details of my runs from any device, and share them with friends who do not need to be logged on to the app or its Web site to view them. I also want to be able to run without having to take my phone with me, even though I sometimes do, especially when I am traveling.

I have gone through a number of running apps on my Apple Watch. The requirement to be able to run with the watch and without the iPhone made the initial choice very limited. I started with Apple’s Activity app that comes with the watch. While the app has a nice interface and can be used without being tethered to the iPhone, its inability to export data beyond the iPhone makes it a non-starter. When I upgraded my iPhone shortly after obtaining my Apple Watch 2 last year, and promptly lost all of my runs that had been stored on my old iPhone, I had no way to get them back.

Within the first few months of the Apple Watch 2 release, some of the other running app vendors released standalone versions of their apps. One of the first was RunGo. It was a decent app, although one has to explicitly save runs to the cloud as “My Routes,” as it is not done automatically. Nonetheless, RunGo has served me well in places such as Singapore, Bangkok, Honolulu, Siesta Key FL, Chicago, Philadelphia, and here in Portland (including my annual birthday 10-mile run).

More recently, I have settled on Strava, a long-time running and cycling app that by default stores exercise sessions in the cloud. The Strava Apple Watch app is not perfect. I wish the watch app displayed the cumulative distance run in hundredths of miles (instead of tenths) and the cumulative time and distance in larger size on the watch than the pace. I do like its auto-stop abilities for when I get stuck at stop lights, although get a little bit annoyed when it stops temporarily when I pull up the watch to view my distance and time. Strava too has served me well in a number of places, including Washington DC, New York City, and Abu Dhabi. All in all, I will stick with Strava for now.

Some may wonder whether I have considered upgrading to the Apple Watch 3, whose main feature is including a cellular chip so it can be accessed without being tethered to the iPhone. Given my running needs, it may seem ironic that I do not see a need to get the new watch. This is because with the exception of being out on my runs, I am just about always carrying my phone, so see no need to have the watch stand alone at other times I am not exercising.

Monday, November 20, 2017

From Predictive to Prescriptive Analytics: Response to NLM RFI

The National Library of Medicine (NLM) recently posted a Request for Information (RFI) asking for comment on promising directions and opportunities for next-generation data science challenges in health and biomedicine. This blog posting lists the questions posed and my responses to them. A main focus of my input centers on the need for transition from predictive to prescriptive analytics, i.e., going beyond being able to predict with data and moving toward applying it to improve patient diagnoses and outcomes.

1. Promising directions for new data science research in the context of health and biomedicine.  Input might address such topics as Data Driven Discovery and Data Driven Health Improvement.

The scientific literature is increasingly filled with papers describing novel and exciting applications of data science, such as improving clinical diagnosis and determining safer and more efficient healthcare. But there is more to impactful data science than the data and tools. We need studies that demonstrate real impact in improve patient and system outcomes. We need to assess the impact of efforts improving data standards and data quality.

One way to look at this is to consider the growing area of data analytics, which may be thought of as applied data science. Data analytics classifies three levels of analytics [1]:
  • Descriptive - describing what the data say about what has happened
  • Predictive - using the data to predict what might happen going forward
  • Prescriptive - deciding on actions based on the data to improve outcomes
Of course, there is science behind each level. We are seeing a steady stream of scientific papers on the application of predictive analytics. One of the earliest foci was the use of clinical data to predict hospital readmission, especially as a result of the Centers for Medical and Medicaid Services (CMS) penalizing US hospitals for excessive rates of readmission. This has led to dozens of papers being published over the last decade assessing various models and approaches for prediction of hospital readmission, e.g., [2,3]. Another focus that has recently attracted attention has been the use of deep learning for medical diagnoses through processing of radiology [4,5], pathology [6], and photographic images [7]. Even patient monitoring and health behaviors have shown the potential benefit for improvement via Big Data [8]. Likewise, as the database for precision medicine emerges, we will understand increasingly data-driven ways to treat different diseases, sometimes by therapies we never hypothesized for a given condition [9].

These predictive analytics applications are important, but equally important is research into how they will be best applied. Attention to hospital readmissions has somewhat lowered its rate, but the problem is far from solved. We not only need to predict who these patients will be, but device programs that will enable action on that data.

Likewise, as we learn to improve diagnosis and treatment of disease through predictive analytics, we will need to determine ways to make actions on those predictions possible, both for clinical researchers who discover new possible diagnostic tests and treatments for disease as well as clinicians who apply the new complex information in patient care. This will require both clinical decision support from machines and new organizational structures to conduct research and apply its results optimally in clinical care.

As such, a new thread of research in prescriptive analytics, i.e., applying the outcomes of data science research, is critical for realizing the value of biomedical science. The NLM should be at the forefront of thought leadership and funding of that research. Such research can build on its unique strong portfolio of existing research in biomedical informatics (which some of us consider data science to be a part of).

2. Promising directions for new initiatives relating to open science and research reproducibility. Input might address such topics as Advanced Data Management and Intelligent and Learning Systems for Health.

Open science and reproducibility of research are critical for the transition of data science from predictive to prescriptive analytics. Since the value of data science comes from large understanding of populations of patients, it is only fair to all who contribute their data to benefit from research using it. Therefore, we must devise methods to allowing appropriate access to that data while still protecting the privacy of individuals who have contributed their data. We also need to devise approaches to give appropriate scientific credit to those who collect the data, and a short time-limited window for them to achieve the first publication of results from it.

Open science should not, however, just be thought of as open data. The models and algorithms that process such data are also increasingly complex. We need more research into understanding how such systems work, how different methods compare with each other, and where biases and other problems may be introduced. As such, the algorithms used must be open so they can be understood and improved.

3. Promising directions for workforce development and new partnerships. Input might address such topics as Workforce Development and Diversity and New Stakeholder Partnerships.

New directions in data science must take into account the human workforce needed to lead discovery as well as apply it to achieve value. The best known data analytics workforce analyses from McKinsey [10] and IDC [11] are a few years old now, but both make a consistent point that we not only need a focused cadre of quantitative experts, but also 5-10 fold more professionals who can contribute to the design of analyses and apply their results in ways that improve patient and system outcomes. In other words, we need individuals who not only know the optimal methods for predictive uses, but also domain experts and applications specialists who can collaborate with the quantitative experts to achieve the best outcomes of data science.

In conclusion, there are many opportunities to put data science and data analytics to work for advancing health and healthcare. This work must not only build on past work done in biomedical informatics and other disciplines but also look to the future to best apply prediction in ways that improves maintanence of health and treatment of disease.

References

1. Davenport, TH (2015). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Cambridge, MA, Harvard Business Review.
2. Amarasingham, R, Moore, BJ, et al. (2010). An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Medical Care. 48: 981-988.
3. Futomaa, J, Morris, J, et al. (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics. 56: 229-238.
4. Oakden-Rayner, L, Carneiro, G, et al. (2017). Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports. 7: 1648. https://www.nature.com/articles/s41598-017-01931-w.
5. Rajpurkar, P, Irvin, J, et al. (2017). CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. https://arxiv.org/abs/1711.05225.
6. Liu, Y, Gadepalli, K, et al. (2017). Detecting cancer metastases on gigapixel pathology images. https://arxiv.org/abs/1703.02442.
7. Esteva, A, Kuprel, B, et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature. 542: 115-118.
8. Price, ND, Magis, AT, et al. (2017). A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nature Biotechnology. 35: 747-756.
9. Collins, FS and Varmus, H (2015). A new initiative on precision medicine. New England Journal of Medicine. 372: 793-795.
10. 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.
11. Anonymous (2014). IDC Reveals Worldwide Big Data and Analytics Predictions for 2015. Framingham, MA, International Data Corporation. http://bit.ly/IDCBigDataFutureScape2015.

Wednesday, November 8, 2017

End of an Era For Academic Informatics: Demise of the Home-Grown EHR

Pick your cliche to describe a major event this past week: Another domino is falling. The dawn of a new era. The news that Vanderbilt University Medical Center, home to one of the most esteemed academic informatics programs in the country, is replacing its collection of home-grown and commercial electronic health record (EHR) systems with Epic shows that the era of the home-grown academic EHR is coming to a close.

Whatever cliche we wish to use, the change is real for academic informatics. One by one, many of the major academic informatics programs have sunset their home-grown EHRs in favor of commercial systems, including Partners Healthcare, Mayo Clinic, Intermountain Healthcare, and the Veteran’s Administration.

The enterprise EHR has become too complex for a single academic program to maintain. Academic informatics programs are great at fostering innovation in areas such as clinical decision support and re-use of clinical data. But they are less adept at managing the more mundane yet increasingly complex operations of hospitals and healthcare systems, such as the transmission of orders from the hospital ward to departmental (e.g., radiology or pathology) systems, the delivery of results back to clinicians, and the generation of bills for services. When compliance and security issues are added on top, it becomes untenable for academic programs to maintain.

Some in academic informatics lament this closing of an era. But ever the glass-half-full optimist, I do not necessarily view it as a bad thing. Now that EHR systems are mission-critical to healthcare delivery organizations and must be integrated with their myriad of other information systems, it is probably inappropriate for academic groups to develop and maintain them.

Fortunately, there are emerging tools for innovation on top of the mundane “plumbing” of the EHR. Probably the leading candidate to serve as such a platform is SMART on FHIR. A growing number of academic programs are using SMART on FHIR to innovate on top of commercial EHRs. Granted, some of the commercial EHR systems (e.g., Epic) currently support the Fast Health Interoperability Resources (FHIR) standard incompletely, but we can remember another cliche, which is the famous Wayne Gretzky quote of skating not to where the puck is, but where it will be going. As SMART on FHIR matures, I can envision it as a great platform for apps that read and write data from the EHR.

In some ways I liken the situation to the relationship between computer operating systems and academic computer science departments. Very few academic computer scientists do research on operating systems these days. Most academic computer scientists, just like the rest of us, use Windows, MacOS, Linux, iOS, and/or Android. Today’s modern operation systems are complex and require large companies to maintain. Most academic computer science research now occurs on top of those operating systems. There, academics can carry out their innovation knowing that the operating systems (to the best of their capabilities) can manage the data in files, connect to networks, and keep information secure.

This new environment should lead to new types of innovations in informatics, which take place on top of commercial EHRs, which may now be better viewed as the “operating system” that provides the foundational functionality upon which academic informatics innovators can build. This could be a boon to places like my institution, which never even had a home-grown EHR. We are certainly pursuing SMART on FHIR development with rigor going forward.