My thoughts on the MIT Solve Talks at Google Hosted by Kara Miller on Healthcare

MIT Solve Talks at Google Hosted by Kara Miller
Video: https://youtu.be/MpMrK2nm3pU

Streamed live on 28 Oct 2015

Guests:

  • Rushika Ferdandopulle, CEO of Iora Health
  • Denny Ausiello, Chairman Emeritus at Mass General Hospital
  • Heidi Williams, MIT

About Solve: “Learn about MIT’s initiative that asks extraordinary people to work together to find solutions to the extraordinarily hard problems facing our global community.” http://solve.mit.edu

My take home message was that Healthcare needs to consider building these Healthcare Operating Systems Platforms that leverages Big Data and other new technology of this digital era to integrate between sources of data, the practise and the patients. It will allow collaboration between clinicians and patients both in the provision of care and in research.

My Observations about the speakers

  1. Heidi has a very typical data scientist and epidemiologist perspective, cautious and tempered by date
  2. Denny has a very typical experienced practitioner perspective, interested in reform but cautious about hype around revolutions.
  3. Rushika has a next generation medical conceirge or patient advocate kind of perspective.
  4. The host Kara does not have deep enough knowledge of the domain to truly leverage on the panels knowledge, but she does a good job. The panel however don’t always take her queues, as evidenced in the Watson comments.
  5. The overal theme and consensus is the need for leveraging new IT capabilities to provided more wholistic lifetime medical records to clinicians at the point of care so that evidence based medicine can become the norm of practise.

My Observations about the issues

  1. Issue raised in 5:35, US using biggest % of GDP in healthcare but not getting as good quality measures as some other nations who spend less.
  2. In 7:50 its established that the driver of inflated healthcare cost in the US is waste. One driver of waste is fee for service, which skews incentives to provision unecessary procedures and therapies.
  3. The story from 17:54 by Rushika demonstrates the issue of fee for service procedure pushing and upselling that needs a better and stronger primary care doctor to prevent. At the heart of the issue is a lack of a patient advocate and the information assymetry
  4. Comments from Denny in 23:06 onwards show the need for good data science and epidemiology to avoid inaccurate misconceptions and generalisations
  5. Comments from Denny in 26:42 bring up the culture of the US, and how it rejects ‘benign neglect’, where accepting less is acceptable, but it the US, patients are vigilant and expect more. He goes on to say that medicine today is probably only operating on a 50% ratio on evidence based, the other 50% of blindness is caused by lack of information.
  6. Key point from Denny in 28:00 “The quality and quantity we get from our patients at the point of care is quite random and episodic”, its in this absence of information that a physician may resort to basing their decisions on their personal expereince rather then the nature of evidence that is available.
  7. 3:35 Kara: “how do we get from 50% to 80%?” Denny: “The quality of that information has to be guarded under more continous and presymtomatic ways” – He goes on to say that we need to use the digital tools of today to capture all phenotype information to provide clinicians with the information needed to make informed decisions. “We need a complete retake on how we garner these information, how do we partner with our patients not only in clinical care but in discovery, and then how we annotate that information to give a much more evidence based and scientific base to medicine.”
  8. 34:35 Rushika: “The right way to do this obviously is to get a tonne of data in from when people are living their normal life, we have to figure out how we interpret that data, how do we pick signals out of the noise, and turn that into action”
  9. When Kara talks about IBM’s Watson in 36:20, Denny responds to say Watson is good for dealing with structured data but not unstructured data. I know IBM’ers who will jump at this statement, but I think Denny’s point here is that until Watson can be part of ingesting and consuming data from the points of care and make sense of it depsite its lack of structure and ontology, it will be relegated to studdying journals and already structured medical knowlegde and correlating that to post structured content created by clinicians.
  10. Denny paints a picture from 37:20 of a scenario where we are able to process the data glut and turn it into a data resource that includes journaling and participation from patients, then then turned to knowledge and actions. The market now is full of apps that are comodities, that are not prioritized for goals of precision and not intergrated into the overall patient record. “We need a fully integrated and wholistic system”
  11. 39:49 Heidi points out that IT has failed to be the magic bullet to solve issues as promised
  12. 40:52 Rushika explains that the reason for this failure of IT to deliver has been rooted in the fact that much of the systems built were pivoted on billing and with that focus, the ROI and gains were focused on billing optimisation, and therefore they seeked to make Doctors structure on input, turning their documentation from a simple note in plain english to 50 clicks of forms and severely driving down productivity.
  13. 42:40 Denny: “we were all trained to diagnose disease and treat disease and its progression to ultimately death, we are the only profession in the world that doesn’t know its gold standard, we can’t diagnose wellness”. To drive wellness and engage patients, we need to work on defining what wellness looks like.
  14. Comments till 48:00 on the theme of whoslistic planning for policy makers and factoring the social aspect for health, to be able to meet the public policy goals they typically have.
  15. 49:00 Denny: “Partnerships with patients, not just in care, but also in discovery”. Behaviour science is a science, and its something to master to modify behaviour. Read Social Physics http://goo.gl/IGPaJw
  16. 57:29 Rushika describes Iora as building an operating system for healthcare instead of an EHR, a link between technology and people. Not billing but collaborative care. “Technology in the context of realtionship”
  17. 58:56 Denny: “Integrated Healthcare Systems” – “Intergration depends on people not machines, BUT machines, toolkits and skillsets can ehance much of that, and we would be foolish living in such a technologically advanced era in not taking advantage of that”

Herding Cats: Rethinking our change management strategy

Herding Cats 

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It has been repeated ad nauseam that “working in healthcare IT is like herding cats“, a refrence to the challenges to faced in change management of Clinicians and other supporting actors in the provision of care. In their paper “Herding Cats: The Challenges of EMR Vendor Selection [1]”.  Doctors McDowell & Michelson remind us that in the case of migrating to an EMR;

“In some instances, the process may represent only an incremental change in a partially developed computerised EMR. In other cases, it comes closer to a revolution, as it is part of a complete overhaul of a minimally computerised medical record system. In the latter circumstance, the implementation of the EMR involves much more than simply automation of preexisting processes. Strategically it requires analysis of, and change to, the underlying clinical information processes.” 

In other words, it requires a change to the actual practise of care and naturally there will be resistance from your clinicians. Which is why the authors rather cheekily allude to the herding of cats in their title.

The Value of Information 

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Fundamental to the practise of medicine is the medical record. The practise has evolved to codify knowledge, track patients as individuals, part of a cohort or on an epidemiological scale and track studies and research before the information revolution – so the question is often set up wrongly,as “do we need to go paperless?”. The real question should be how much faster, more collaborative, more comprehensive, more accessible simultaneously and more persistent and available do we need our medical record to be? Popular culture is saturated enough by IT for all to understand the value of information through software, so while everyone looks looking up information on a computer and the added benefits of analytics it affords, many dislike the disruptive nature of the EMR is making providers change their workflows, be more disciplined with documentation and having to do things in certain methodologies or process steps. Resistance then often comes not from a hatred of screens or keyboards but the intrusivenes of having someone else dictate your methodology and process.

Rethinking our Change Management Strategy 

electronic_medical_records

The conventional wisdom is usually to engage clinicians at the very begining and then buy some monolithic application that does everything from billing claims to medical records and struggle with integration to the myriad of anxilliary life science software that already exist such as medical imaging, pharmacy management & laboratory systems. What ends up happening is either a paralysis of choosing a system or a fallout from Clinicians who lost the vote and then provide resistance to the change that the chosen software will bring to their work.

I have from my experience adopted a different approach. Let me give you a high level overview to get you thinking;

0. Build a decent IT department with real IT experts because no matter what you choose, the fundamentals underlying everything is IT.

1. The Content Management Phase. Let those experts work on a data integration strategy – how to build a complete 360 view of a patient, from operational, financial and medicine that the different stakeholders can use at the point of need to access all the information they need about the patient their attending to. This will involve the digitisation of legacy records, from scanned images, to Optical Character Recognition and patching in existing digital information that already exist. What they will end up building is a digitisation bureau and a data warehouse that will be able to provide a consolidated patient record to any application you choose to use later.

2. The Analytics & Automation Phase. Avoid talking about new workflows and process, rather begin by providing Clinicians and Operational staff more and more access to patient centered information at their convenience on their computers and mobile devices in a secure and reliable fashion. Quickly churn out analytics from this data warehouse such as some basic measures of outcomes, productivity or even commercial insights such as revenue drivers, performing departments and efficiency of different support services.

3. The New Workflows & Standards Phase. Only change the method of input and data capture with new workflows and tools after steps 1 to 3 are established. The added benefit of having completed step 1 is that you now have a bigger selection of applications that can be used since all of them tap the record from the common data warehouse. Many experienced healthcare people reading this now will protest that this can’t be done, but honestly if step 0 is done correctly, we won’t need to have this debate.

Screenshot 2014-06-02 15.16.20

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[1] “Herding Cats: The Challenges of EMR Vendor Selection” by Samuel W. McDowell, PhD, Regi Wahl, and James Michelson, MD in the  Journal of Healthcare Information Management — Vol. 17, No. 3

Zen: Beaten Paths reveal human behavior

An Architect (civil) friend told me a story about working around human behavior  that has stuck with me over the years and influenced my practice as an Architect of information systems.

The story is about an architect who was looking for a solution to the persistent problem of people not using sidewalks no matter how convenient they were. Eventually the foot traffic wore out new paths on the landscape and would be an eyesore.This architect had an idea, he would build all of his buildings, but defer the sidewalks. He would just plant grass. 6 months later he would come back and put sidewalks down where all the beaten paths emerged. By doing this he put the paths in the places that emerged from unpredictable trends of human behavior. This was the failure of all his counterparts, they were trying to predict those trends, and often got it wrong.

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The principle that I applied to my field of work is to leave interfaces and parts of information systems to the trends that emerge, rather than trying to dictate something that people wont use. The modern enterprise is a combination of policies, processes and services that have been designed top down, but they should meet grassroots movements and trends halfway, for maximum impact.

The story also carries a fashionable new big data lesson – if we can understand trends we can capitalize on actual human behaviour, rather than our inaccurate traditions of conjecture.