February 11, 2016 § 6 Comments
Have we reached peak e-health yet?
Anyone who works in the e-health space lives in two contradictory universes.
The first universe is that of our exciting digital health future. This shiny gadget-laden paradise sees technology in harmony with the health system, which has become adaptive, personal, and effective. Diseases tumble under the onslaught of big data and miracle smart watches. Government, industry, clinicians and people off the street hold hands around the bonfire of innovation. Teeth are unfeasibly white wherever you look.
The second universe is Dickensian. It is the doomy world in which clinicians hide in shadows, forced to use clearly dysfunctional IT systems. Electronic health records take forever to use, and don’t fit clinical work practice. Health providers hide behind burning barricades when the clinicians revolt. Government bureaucrats in crisp suits dissemble in velvet-lined rooms, softly explaining the latest cost overrun, delay, or security breach. Our personal health files get passed by street urchins hand-to-hand on dirty thumbnail drives, until they end up in the clutches of Fagin like characters.
Both of these universes are real. We live in them every day. One is all upside, the other mostly down. We will have reached peak e-health the day that the downside exceeds the upside and stays there. Depending on who you are and what you read, for many clinicians, we have arrived at that point.
The laws of informatics
To understand why e-health often disappoints requires some perspective and distance. Informed observers again and again see the same pattern of large technology driven projects sucking up all the e-health oxygen and resources, and then failing to deliver. Clinicians see that the technology they can buy as a consumer is more beautiful and more useful that anything they encounter at work.
I remember a meeting I attended with Branko Cesnik. After a long presentation about a proposed new national e-health system, focusing entirely on technical standards and information architectures, Branko piped up: “Excuse me, but you’ve broken the first law of informatics”. What he meant was that the most basic premise for any clinical information system is that it exists to solve a clinical problem. If you start with the technology, and ignore the problem, you will fail.
There are many corollary informatics laws and principles. Never build a clinical system to solve a policy or administrative problem unless it is also solving a clinical problem. Technology is just one component of the socio-technical system, and building technology in isolation from that system just builds an isolated technology .
Breaking the laws of informatics
So, no e-health project starts in a vacuum of memory. Rarely do we need to design a system from first principles. We have many decades of experience to tell us what the right thing to do is. Many decades of what not to do sits on the shelf next to it. Next to these sits the discipline of health informatics itself. Whilst it borrows heavily from other disciplines, it has its own central reason to exist – the study of the health system, and of how to design ways of changing it for the better, supported by technology. Informatics has produced research in volume.
Yet today it would be fair to say that most people who work in the e-health space don’t know that this evidence exists, and if they know it does exist, they probably discount it. You might hear “N of 1” excuse making, which is the argument that the evidence “does not apply here because we are different” or “we will get it right where others have failed because we are smarter”. Sometimes system builders say that the only evidence that matters is their personal experience. We are engineers after all, and not scientists. What we need are tools, resources, a target and a deadline, not research.
Well, you are not different. You are building a complex intervention in a complex system, where causality is hard to understand, let alone control. While the details of your system might differ, from a complexity science perspective, each large e-health project ends up confronting the same class of nasty problem.
The results of ignoring evidence from the past are clear to see. If many of the clinical information systems I have seen were designed according to basic principles of human factors engineering, I would like to know what those principles are. If most of today’s clinical information systems are designed to minimize technology-induced harm and error, I will hold a party and retire, my life’s work done.
The basic laws of informatics exist, but they are rarely applied. Case histories are left in boxes under desks, rather than taught to practitioners. The great work of the informatics research community sits gathering digital dust in journals and conference proceedings, and does not inform much of what is built and used daily.
None of this story is new. Many other disciplines have faced identical challenges. The very name Evidence-based Medicine (EBM), for example, is a call to arms to move from anecdote and personal experience, towards research and data driven decision-making. I remember in the late ‘90s, as the EBM movement started (and it was as much a social movement as anything else), just how hard the push back was from the medical profession. The very name was an insult! EBM was devaluing the practical, rich daily experience of every doctor, who knew their patients ‘best’, and every patient was ‘different’ to those in the research trials. So, the evidence did not apply.
EBM remains a work in progress. All you need to do today is to see a map of clinical variation to understand that much of what is done remains without an evidence base to support it. Why is one kind of prosthetic hip joint used in one hospital, but a different one in another, especially given the differences in cost, hip failure and infection? Why does one developed country have high caesarian section rates when a comparable one does not? These are the result of pragmatic ‘engineering’ decisions by clinicians – to attack the solution to a clinical problem one way, and not another. I don’t think healthcare delivery is so different to informatics in that respect.
Is it time for evidence-based health informatics?
It is time we made the praxis of informatics evidence-based.
That means we should strive to see that every decision that is made about the selection, design, implementation and use of an informatics intervention is based on rigorously collected and analyzed data. We should choose the option that is most likely to succeed based on the very best evidence we have.
For this to happen, much needs to change in the way that research is conducted and communicated, and much needs to happen in the way that informatics is practiced as well:
- We will need to develop a rich understanding of the kinds of questions that informatics professionals ask every day;
- Where the evidence to answer a question exists, we need robust processes to synthesize and summarize that evidence into practitioner actionable form;
- Where the evidence does not exist and the question is important, then it is up to researchers to conduct the research that can provide the answer.
In EBM, there is a lovely notion that we need problem oriented evidence that matters (POEM)  (covered in some detail in Chapter 6 of The Guide to Health Informatics). It is easy enough to imagine the questions that can be answered with informatics POEMs:
- What is the safe limit to the number of medications I can show a clinician in a drop-down menu?
- I want to improve medication adherence in my Type 2 Diabetic patients. Is a text message reminder the most cost-effective solution?
- I want to reduce the time my docs spend documenting in clinic. What is the evidence that an EHR can reduce clinician documentation time?
- How gradually should I roll out the implementation of the new EHR in my hospital?
- What changes will I need to make to the workflow of my nursing staff if I implement this new medication management system?
EBM also emphasises that the answer to any question is never an absolute one based on the science, because the final decision is also shaped by patient preferences. A patient with cancer may choose a treatment that is less likely to cure them, because it is also less likely to have major side-effects, which is important given their other goals. The same obviously holds in evidence-based health informatics (EBHI).
The Challenges of EBHI
Making this vision come true would see some significant long term changes to the business of health informatics research and praxis:
- Questions: Practitioners will need develop a culture of seeking evidence to answer questions, and not simply do what they have always done, or their colleagues do. They will need to be clear about their own information needs, and to be trained to ask clear and answerable questions. There will need to be a concerted partnership between practitioners and researchers to understand what an answerable question looks like. EBM has a rich taxonomy of question types and the questions in informatics will be different, emphasizing engineering, organizational, and human factors issues amongst others. There will always be questions with no answer, and that is the time experience and judgment come to the fore. Even here though, analytic tools can help informaticians explore historical data to find the best historical evidence to support choices.
- Answers: The Cochrane Collaboration helped pioneer the development of robust processes of meta-analysis and systematic review, and the translation of these into knowledge products for clinicians. We will need to develop a new informatics knowledge translational profession that is responsible for understanding informatics questions, and finding methods to extract the most robust answers to them from the research literature and historical data. As much of this evidence does not typically come from randomised controlled trials, other methods than meta-analysis will be needed. Case libraries, which no doubt exist today, will be enhanced and shaped to support the EBHI enterprise. Because we are informaticians, we will clearly favor automated over manual ways of searching for, and summarizing, the research evidence . We will also hopefully excel at developing the tools that practitioners use to frame their questions and get the answers they need. There are surely both public good and commercial drivers to support the creation of the knowledge products we need.
- Bringing implementation science to informatics: We know that informatics interventions are complex interventions in complex systems, and that the effect of these interventions vary depending on the organisational context. So, the practice of EBHI will of necessity see answers to questions being modified because of local context. I suspect that this will mean that one of the major research challenges to emerge from embracing EBHI is to develop robust and evidence-based methods to support localization or contextualisation of knowledge. While every context is no doubt unique, we should be able to draw upon the emerging lessons of implementation science to understand how to support local variation in a way that is most likely to see successful outcomes.
- Professionalization: Along with culture change would come changes to the way informatics professionals are accredited, and reaccredited. Continuing professional education is a foundation of the reaccreditation process, and provides a powerful opportunity for professionals to catch up with the major changes in science, and how those changes impact the way they should approach their work.
There comes a moment when surely it is time to declare that enough is enough. There is an unspoken crisis in e-health right now. The rhetoric of innovation, renewal, modernization and digitization make us all want to believers. The long and growing list of failed large-scale e-health projects, the uncomfortable silence that hangs when good people talk about the safety risks of technology, make some think that e-health is an ill-conceived if well intentioned moment in the evolution of modern health care. This does not have to be.
To avoid peak e-health we need to not just minimize the downside of what we do by avoiding mistakes. We also have to maximize the upside, and seize the transformative opportunities technology brings.
Everything I have seen in medicine’s journey to become evidence-based tells me that this will not be at all easy to accomplish, and that it will take decades. But until we do, the same mistakes will likely be rediscovered and remade.
We have the tools to create a different universe. What is needed is evidence, will, a culture of learning, and hard work. Less Dickens and dystopia. More Star Trek and utopia.
Since I wrote this blog a collection of important papers covering the important topic of how we evaluate health informatics and choose which technologies are fit for purpose has been published in the book Evidence-based Health Informatics.
- Slawson DC, Shaughnessy AF, Bennett JH. Becoming a medical information master: feeling good about not knowing everything. The Journal of Family Practice 1994;38(5):505-13
- Tsafnat G, Glasziou PP, Choong MK, et al. Systematic Review Automation Technologies. Systematic Reviews 2014;3(1):74
- Coiera E. Four rules for the reinvention of healthcare. BMJ 2004;328(7449):1197-99
February 8, 2016 § Leave a comment
Every year the body of research evidence in health informatics grows. To stay on top of that research, you need to know where to look for research findings, and what the best quality sources of it are. If you are new to informatics, or don’t have research training, then you may not know where or how to look. This page is for you.
There are a large number of journals that publish only informatics research. Many mainstream health journals will also have an occasional (and important) informatics paper in them. Rather than collecting a long list of all of these possible sources, I’d like to offer the following set of resources as a ‘core’ to start with.
(There are many other very good health informatics journals, and their omission here is not meant to imply they are not also worthwhile. We just have to start somewhere. If you have suggestions for this page I really would welcome them, and I will do my best to update the list).
If you require an overview of the recent health informatics literature, especially if you are new to the area, then you really do need to sit down and read through one of the major textbooks in the area. These will outline the different areas of research, and summarise the recent state of the art.
I am of course biased and want you to read the Guide to Health Informatics.
A collection of important papers covering the important topic of how we evaluate health informatics and choose which technologies are fit for purpose can be found in the book Evidence-based Health Informatics.
Another text that has a well-earned reputation is Ted Shortliffe’s Biomedical Informatics.
Health Informatics sits on the shoulders of the information and computer sciences, psychology, sociology, management science and more. A mistake many make is to think that you can get a handle on these topics just from a health informatics text. You wont. Here are a few classic texts, from these ‘mother’ disciplines;
Computer networks (5th ed). Tannenbaum and Wetherall. Pearson. 2010.
Engineering Psychology & Human Performance (4th ed.). Wickens et al. Psychology Press. 2012.
Artificial Intelligence: A Modern Approach (3rd ed). Russell and Norvig. Pearson. 2013
Google Scholar: A major barrier to accessing the research literature is that much of it is trapped behind paywalls. Unless you work at a university and can access journals via the library, you will be asked by some publishers to pay an exorbitant fee to read even individual papers. Many journals are now however open-access, or make some of their papers available free on publication. Most journals also allow authors to freely place an early copy of a paper onto a university or other repository.
The most powerful way to finding these research articles is Google Scholar. Scholar does a great job of finding all the publicly available copies of a paper, even if the journal’s version is still behind a paywall. Getting yourself comfortable with using Scholar, and exploring what it does, provides you with a major tool for accessing the research literature.
Yearbook of Medical Informatics. The International Medical Informatics Association (IMIA) is the peak global academic body for health informatics and each year produces a summary of the ‘best’ of the last year’s research from the journals in the form of the Yearbook of Medical Informatics. The recent editions of the yearbook are all freely available online.
Next, I’d suggest the following ‘core’ journals for you to skim on a regular basis. Once you are familiar with these you will no doubt move on the the very many others that publish important informatics research.
JAMIA. The Journal of the American Medical Informatics Association (AMIA) is the peak general informatics journal, and a great place to keep tabs on recent trends. While it requires a subscription, all articles are placed into open access 12 months after publication (so you can find them using Scholar) and several articles every month are free. You can keep abreast of papers as they are published through the advanced access page.
JMIR. The Journal of Medical Internet Research is a high impact specialist journal focusing on Web-based informatics interventions. It is open access which means that all articles are free.
To round out the journals you might want to add into your regular research scan the following journals which are all very well regarded.
- Journal of Biomedical Informatics, which focuses more on methods than the other journals.
- International Journal of Medical Informatics, which tries to cover informatics issues with a global perspective.
- Artificial Intelligence in Medicine, which as the name suggest is focussed on advance topics in decision support and analytics.
- BMC Medical Informatics and Decision Making, which is an open access member of the BMC family of journals
- Methods of Information in Medicine, specialises in informatics methodologies.
Whilst journals typically will publish well polished work, there is often a lag of a year or more before submitted papers are published. The advantage of research conferences is that you get more recent work, sometimes at an earlier stages of development, but also closer to the cutting edge.
There are a plethora of informatics conferences internationally but the following publish their papers freely online, and are typically of high quality.
AMIA Annual Symposium. AMIA holds what is probably the most prestigious annual health informatics conference, and releases all papers via NLM. An associated AMIA Summit on Translational Sciences/Bioinformatics is also freely available.
Medinfo. IMIA holds a biannual international conference, and given its status as the peak global academic society, Medinfo papers have a truly international flavour. Papers are open access and made available by IOS press through its Studies in Health Technology and Informatics series (where many other free proceedings can be found). Recent Medinfo proceedings include 2015 and 2013.
As with textbooks and journals, it is worth remembering that much of importance to health informatics is published in other ‘mother’ disciplines. For example it is well worth keeping abreast of the following conferences for recent progress:
The ACM Digital Library, which contains WWW, is a cornucopia of information and computer science conference proceedings. Many a rainy weekend can be wasted browsing here. You may need to hunt the web site of the actual conference however to get free access to papers as ACM will often try to charge for papers you can find freely on the home page of the conference.
Browsing journals is one way to keep up to date. The other is to follow the work of individual researchers whose interests mirror your own. The easiest way to do this is to find their personal page on Google Scholar (and if they don’t have one tell them to make one!). Here is mine, as an example. There are two basic ways to attack a scholar page. When you first see a Scholar page, the papers are ranked by their impact (as measured by other people citing the papers). This will give you a feeling about the work the researcher is most noted for. The second way is to click the year button. You will then see papers in date order, starting with the most recent. This is a terrific way of seeing what your pet researcher has been up to lately.
There is a regularly updated list of biomedical informatics researchers, ranked by citation impact, and this is a great way to discover health informatics scientists. Remember that when researchers work in more specialised fields, they may not have as many citations and so be lower down the list.
Once you find a few favourite researchers, try to see what they have done recently, follow them on Twitter, and if they have a blog, try to read it.
October 7, 2015 § 2 Comments
Learning health systems are the next big thing. Through the use of information technology, the hope is that we can analyse all the data captured in electronic health records to speed both the process of scientific discovery and the translation of these discoveries into routine practice1,2. Every patient’s data, their response to treatment, and final outcome, will no longer be filed away, but feed the care of future patients3. It’s an exciting vision, and if we can achieve it, there is no doubt healthcare delivery would be transformed.
If we were to step back, we might conclude that although this is an admirable vision, for all its failings, the machinery of science is already working faster than we can handle it. The arena where organizational learning most needs to take hold is in the way we deliver health services. It is clear that we could do so much better in this arena. There is too much variation in patient care, too much waste and harm in the system.
So, if what we have today is not yet a learning health system, then by definition we must have the opposite – a forgetting health system. If that is the case, then here are two working definitions to contrast the two ideas:
- Learning system: The past shapes the future. Today’s mistake is tomorrow’s wisdom.
- Forgetting system: The past is the future. Today’s mistakes are forgotten quickly and are repeated tomorrow.
With this perspective it is easy to see examples everywhere of such ‘forgetting’. The history of large-scale e-health is a litany of the same case study being repeated. A large health IT project is started (usually by a government and usually as a technology innovation project rather than to fix a defined health problem). It quickly runs over time, over budget, and is treated with dismay as its users find it doesn’t do what they were promised. The end result is new problems, workarounds to circumvent the intruder technology, and in some cases, the eventual removal of the system.
The solution to this mess is of course seems to be to start a new large-scale e-health system, run by different people. Yet, like moths to a flame, these new protagonists seem to make the same set of strategic mistakes, but in new ways. Today’s large-scale health IT projects seem to be in a perpetual state of Groundhog Day, and must be a classic example of a forgetting system. This may be because there is no learning, because we convince ourselves that this time is ‘different’ and the past has nothing to teach us, or because when we look at past failures, we are unable to drawn any conclusions because of a lack of ability or imagination.
Information is lost
If a system ‘forgets’ then by implication that means that information that existed in the system has been lost, preventing its reuse to guide future events. If you think about it for one moment, the health system loses information every moment of every day, in unimaginable amounts. We only record a fraction of the events that occur. Most of these are not directly captured but rather are reconstructed from the memory of the individual making the record.
If we were to transform our forgetting system into a learning system, so that health system improvements are driven by experience, what do we need to do? For a system to ‘remember’ an event, a teachable moment, it firstly needs to be detected. You can’t remember what you never saw. Next, after detection, it needs to be recorded. Finally, this recording needs to be somehow aggregated with other events, for discovery of new knowledge to occur.
When it comes to treatment and diagnosis, we have built very precise mechanisms to detect important events or processes through a variety of diagnostic methods. The data captured are increasingly being recorded in electronic systems, and analysed. That’s the ‘big data’ movement in healthcare in a nutshell.
When it comes to health services, we still don’t know what events we should detect, what processes of service delivery should be instrumented, and there is still little thought to pooling system measurements in a way to allow across organizational analysis and learning. So, if we are to make our health services become learning ones, the task is much bigger than installing patient records.
One of the challenges to learning in health services is that, whilst it is easy to imagine that organizational memories can be stored in bits on a database, more often they will sit in the heads of those who work within them4. So, a good indicator that you are working in a forgetting system is that your organization has a high staff turnover – because it is very likely that when staff walk out the door they also walk with everything they know about the system that they are leaving, and no one has tried to capture that rich web of knowledge (if it could so easily be captured).
When learning also requires forgetting
Memories however also exist in other ways in an organisation. Crucially, they persist in the processes, protocols and built structures of the organisation, and in the workarounds and annotations that happen to physical spaces5. These structural memories are not inert or idly awaiting someone’s analysis. They sit there every moment shaping work, and altering human perceptions, actions and intent.
With time, accreted structural memories can lead to inertia, to an immovable status quo6. These kinds of memories therefore need to be managed. Some need to be revisited and revised, others are the jewels of the past that we should never forget and some, some are impeding the emerging purpose of an organization, and need to be actively forgotten. The need for organizational apoptosis, or active forgetting, is something I’ve written about before6.
From learning to adaptive systems
So, learning clearly is not nearly enough. Recording and analysing, at least as far as organisations are concerned, can only take you so far. The living walls and praxis of an organisation are already busy recording, even learning, but may not make it possible to do anything about what has been learned. For organisational inertia to be broken, and for repeated failures to be avoided, we not only need to learn, we need to actively, wisely, carefully, forget.
That will be a true learning system, because learning systems also need the capacity to change.
- Etheredge LM. A rapid-learning health system. Health affairs. 2007;26(2):w107-w118.
- Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Science translational medicine. 2010;2(57):57cm29-57cm29.
- Gallego B, Walter SR, Day RO, et al. Bringing cohort studies to the bedside: framework for a ‘green button’ to support clinical decision-making. Journal of Comparative Effectiveness Research. 2015(0):1-7.
- Coiera E. When conversation is better than computation. Journal of the American Medical Informatics Association. 2000;7(3):277-286.
- Coiera E. Communication spaces. Journal of the American Medical Informatics Association. May 1, 2014 2014;21(3):414-422.
- Coiera E. Why system inertia makes health reform so hard. British Medical Journal. 2011;343:27-29.
May 12, 2015 § 4 Comments
Dear [insert country name] Government,
E-health is hard. I think we can all agree on that by now. You have spent [insert currency] [insert number] billion on e-health programs of one form or another over the last decade, and no one knows better than you how hard it is to demonstrate that you are making a difference to the quality, safety or efficiency of health care.
You also know that so much of e-health needs to happen in the public domain that, irrespective of your desire to privatise the problem, you will end up holding the can for much of what happens. E-health is your responsibility, and your citizens will, rightly or wrongly, hold you accountable.
It is so hard to get good strategic advice on e-health. You recently commissioned [insert large international consultancy firm] to prepare a new national e-health strategy, and it didn’t come cheap at [insert currency] [insert number] million. In the end it told you nothing you didn’t really already know, but at least you can say you tried.
You also commissioned [insert large international consultancy firm] to prepare a business case to back up that strategy, and it didn’t come cheaply either at [insert currency] [insert number] million. The numbers they came up with were big enough to convince Treasury to fund the national strategy, but deep in your heart of hearts you know you’ll never see a fraction of the [insert currency] promised.
It’s also really hard to find organisations that can deliver nation-scale e-health to time, to budget and of a quality that the professions and the voters all agree it’s a good thing. You want the IT folks who build these systems to understand health care, its needs and challenges, deeply. Just because they can build a great payroll system or website does not qualify them to jump in and manage an e-health project. Do you remember how [insert large IT company] ended up crashing and burning when they took on the [insert now legendary e-health project disaster]? We can all agree that didn’t go as planned, and that you didn’t exactly enjoy the coverage in the press and social media.
What you really want firstly is impartial, cheap and informed expert advice because you are in the end driven to do the right thing. Given the heated and partisan nature of politics, that advice needs to come from safe and trusted individuals. That often means the advice comes from within the tent of government, or from paid consultancies where legal contracts and the promise of future work secure your trust. You also want the IT folks who build your systems to be deeply trained in the complexities of implementing systems for e-health. The health professions, and indeed the voters, also need to be sophisticated enough to understand how to use these systems, and their limitations. That’s going to maximise your chances of success, as well as blunt the uninformed chatter that so often derails otherwise good policy.
Our proposal is a simple one. We suggest you set aside 10% of the E-health budget to train the next generation of e-health designers, builders, and users. Use the funds to resource training programs at the Masters level for future e-health policy leaders, as well as system designers, builders and implementers. Let us provide incentives to include e-health in health profession training both at primary degree and for continuing education. Let us also invest in training the public in the safe and effective use of e-health. Investing in creating a critical mass of skilled people over 5 years will be your best insurance that, when you are again faced with e-health, you have a real chance of doing the right thing.
Given how little outcome you have had for your e-health investments over the last decade, and the harsh reality that little will change over the next, this is a chance to rewrite the script. Invest in people and skills, and you might find that with time e-health isn’t so hard after all.
[insert name of concerned citizen, NGO, or professional association]
September 23, 2014 § Leave a comment
The different ways clinicians interact does not just shape the success of the communication act. Our propensity to interrupt each other, and multitask as we handle communication tasks alongside other duties, has a direct effect on how well we carry out everything we do. Interruption for example has the capacity to distort human memory processes, and lead to memory lapses as well as memory distortions.
Earlier this year I was interviewed by Dr. Robert Wachter, the Editor of the Agency for Healthcare Research and Quality (AHRQ) WebM&M. In that interview we covered the roles that interruption and multitasking play in patient safety, discussing both their risks, as well as strategies for minimising their effects. The interview also looked at the implications these communication and task management styles have for the design of information technologies.
The transcript of the interview as well as the podcast are available here.
A related 2012 editorial on the research challenges associated with interruption appeared in BMJ Quality and safety.
It is clear that our clinical information systems are not designed to be used in busy, interrupt-driven environments, and that they suffer because of it. Not only do they not fit the way people of necessity communicate and work, they lead to additional risks and have the potential to harm patients. It perplexes me that information system designers still work on the blind assumption that their users are giving their full attention to the software systems they have built. E-health systems need to be tolerant of interruption, and must be designed to support recovery from such events. Memory prompts, task markers, and retention of context once an action has been completed, are essential for the safe design of e-health systems.
August 29, 2014 § 2 Comments
My learned and senior informatics colleagues spend much time debating the different professional roles that together are needed to support the practice of informatics (e.g. informatician, informatasist). Over the years I have assembled a set of definitions for these professional roles, as well as allied concepts. I feel this list is unlikely to help the debate at all.
Informatocyst [In-for-mata-s-ist, n] (see also, legacy system). A collection or build up of information, walled off from the greater information system by a barrier of incompatible or aged interchange standards.
Infocystation [In-foh-sis-tay-shun, n] An outbreak of informatocysts. Also, the state of co-existence with these informational endoparasites.
Informablution [In-for-mah-bloo-shun, v]. Ritual cleansing of past errors. Application of holy oils extracted by data analysts from sacred dashboard. Thought to bring enlightenment and understanding of the underlying nature of things. See also Informagic.
Informatocist [In-for-mata-s-ist, n] A practitioner specialising in the identification and removal of informatacysts, trained in the uses of tools such as the infolance (not to be confused with a ‘Standards body’ which is a secret cabal that believes the simple chanting of the names of the holy standards causes informatacysts to spontaneously rupture).
Informationist [In-for-may-shun-ist, n] One who believes that all information was created by God four thousand years ago (see also, Creationist, Infolutionist).
Informatics [In-for-mat-eeks, n] An ancient religion. Also, one divided by zero; Everything; Nothing (origins obscure).
Informagic [In-for-ma-gik, n] Informagical adv. The process by which purchase of a computer immediately improves clinical outcomes. (see also meaningless use, informagical thinking, informagician)
Infomortician [In-fo-mohr-tee-shun, n] A practitioner specialising in the preservation of dead information languages (see also mortician, Cobol, Fortran, MUMPS).
Informatrician [In-for-ma-tree-shun, n] Low caste technologist, responsible for designing, building and maintaing real information systems. Does all the work, gets no recognition or rewards. H-index of zero.
Informatelist [In-for-mat- er-lys-t, n] A collector of information system definitions.
Informately [In-for-mata-lee, n] The collection and study of information systems, standards, and related terms; stamp collecting (see also, terminology). Informatelic, informatelical adj. Informatelicaly adv
Informatology [In-for-ma-tolo-gee, n] Study of the information fundament (see also, proctology, semantics, ontology).
Interoperative [Een-ta-hop-era-teev, n]. Double agent. Works for health services but secretly acts for industry. Preaches tolerance and diversity but may fabricate evidence of infocystation to undermines local systems in favour of industry “standard” products.
Telemethodist [Te-lee-meth-o-deest, n] Member of breakaway missionary sect that eschews belief in information for its own sake, emphasises its delivery to the disadvantaged through speaking tubes.
July 15, 2014 § 7 Comments
It’s now almost 20 years since I started to write the first edition, and over 10 years since I wrote the second. I’m very happy to announce that the text for the updated and much expanded third edition is now completed.
The 3rd Edition of the Guide to Health Informatics comes in paper and e-book versions. Purchase of the print version comes bundled with access to the VitalSource e-book version.
A 20% discount is available when you order it direct from the publisher – just quote code BHP01 at checkout.
You can also buy it from Amazon UK or Amazon US, and other bookstores (ISBN-13:978-1444170498). If you wish to purchase the e-book only, several options are available including a kindle edition, and the VitalSource edition, which offers options including time-limited rental, as well as full purchase, and bulk purchase for classes.
Complimentary textbook e-inspection copies are available to qualifying instructors for review prior to course adoption.
The book has a strong emphasis on demonstrating what works and what does not work in informatics. I have created a new evaluative framework that runs through the book, to help us understand why some classes of intervention appear to work so much better than others. As a taster, the new edition has 34 chapters, and is significantly longer than the 2nd edition. The new chapters are each quite extensive in length, and focus as much as possible on basic concepts and principles, rather than simple narrative descriptions of the topics. New chapters include:
- Information system safety
- Social networks and social media interventions
- Model Building for Decision Support, Data Analysis and Scientific Discovery
- Population surveillance and public health informatics
- Clinical bioinformatics and Personalised medicine
- Consumer Informatics
I want to thank all of those who made so many suggestions to me earlier on about what was needed in the new book. I hope I have covered off the most important topics for you. As always the balance is between creating an introductory work which has some longevity and explores the core concepts needed to understand our discipline with a single and unified voice, or writing an encyclopaedic multi-author work that tries to do everything, but has too many voices, becomes out of date quickly, and overwhelms students. At least for this edition I think we have still managed to keep the book to being a ‘single voice’ overview – although I have had many expert colleagues help me with sourcing and structuring the material and checking what has been written. All the old chapters have had overhauls, most of them very significantly (a lot has happened in the last 10 years).
For those who are looking to use the 3rd edition as a part of a course, here is the new table of contents.
Part 1 – Basic Concepts in Informatics 1. Models 2. Information 3. Systems
Part 2 – Clinical Informatics Skills 4. Communication 5. Structuring 6. Questioning 7. Searching 8. Making decisions
Part 3 – Information Systems in Healthcare 9. Information management systems 10. The Electronic Health Record 11. Designing and evaluating information and communication systems 12. Implementation 13. Information System safety 14. Information economics
Part 4 – Guideline and Protocol-based Systems 15. Guidelines, protocols and evidence-based healthcare 16. Computer-based protocol systems 17. Designing, disseminating and applying protocols
Part 5 – Communication Systems in Healthcare 18. Communication system basics; Interlude – The Internet and World Wide Web; 19. Information and Communication networks 20. Social networks and social media interventions 21. Telehealth and mobile health
Part 6 – Language, Coding and Classification 22. Terms, codes and classification 23. Healthcare terminologies and classification systems 24. Natural language and formal terminology
Part 7 – Clinical Decision Support and Analytics 25. Clinical Decision Support Systems; Interlude – Artificial Intelligence in Medicine; 26. Computational reasoning methods 27. Model Building for Decision Support, Data Analysis and Scientific Discovery
Part 8 – Specialized applications for health informatics 28. Patient monitoring and control 29. Population surveillance and public health informatics 30. Bioinformatics 31. Clinical Bioinformatics and Personalized Medicine 32. Consumer Informatics
And I’ve broken with tradition from the earlier editions, and picked a gorgeous new cover.