It is not the EMR that sucks it is your lack of a information governance strategy

Hospitals are drowning is technological deficits; aging equipment, poor information security, unusable electronic health records and importantly no way to effective share patient records with patients or partners. A recent study shows that two out of three hospitals are not meeting HITECH standards for Health Information Exchanges. The authors note that even though there are fines (see here for HITECH fines), it is unlikely that these will spur adoption of health information exchanges (HIE).

Bernie Monegain (Editor Healthcare IT News) does a great job summarizing the article. From a software vendor perspective this seems like a perfect storm; a gap in capabilities, upcoming deadlines and a change in revenue models. In any other industry there would be lines of vendors at every hospital's CFO's door trumpeting their ability to help them meet their deadlines. Unfortunately the usual suspect vendors in HealthIT have not seized the opportunity (see here).

As I have mention before this is where ECM, WEM, etc vendors should be stepping in to fill the gap.Hospitals of all sizes will need to be able to confidently exchange patient information and make it available to patients once Meaningful use 2 standards for patient accessibility come into affect. Providibng a mechanism to share patient information in a standardized, secure manner is not a nice to have item, it is a required item to meet obligations-it should be on every hospital CIO, CFO and CEO's radar.

It also speaks to the larger problem of what electronic health records are strategically versus the narrow software characterization. Healthcare providers and thought leaders need to acknowledge the software sucks, and is not the best place to share and view information. It is just a dumb database designed to HOUSE patient information in a safe manner- as the name suggests a EHR is part of a records management strategy.

Electronic Patient information has the potential to increase the efficiency and cost effectiveness of healthcare delivery. The problem is the variety of solutions deployed by individual healthcare practices makes integration at the regional and national level difficult. As a rule they have been bought as point solutions to a immediate problem rather than as part of a healthcare information governance strategy.

It is time to look past a single solution that has a single set of technical specifications and build a system that manages data access.

As with any application rationalization process, it is important to define the costs, benefits and integration needs for any new enterprise application. Make no mistake; Health IT can no longer be a single application portfolio, they have to move to an ecosystem approach based on both clinical and administrative needs.

The failure of the single point solution of EHR/EMR has cause many IT professionals to take a negative view of information technology itself. As I have mentioned before, the problem is not the storage of the information it is how to access the information- it is a content management issue-be it ECM, WCM or -gasp-(SharePoint). EHR.EMR systems are horrible at providing access. For meaningful use 3 compliance and for your external marketing you need some kind of content serving system.

For organizations in a position to move to the newest EHR/EMR products, there may be no reason to have an additional system.For everyone who doesn't see a rip and replace in their next five years, consider how all the devices and partnerships that you have (and will have to grow to stay in compliance with Meaningful use).

You have a variety of regulatory items to think about as you develop your information governance strategy:

HIPAA 5010 covers Electronic data exchange(EDI[X12]) compliance standards as mandated for 1/1/2012: It covers exchange of all data transmitted by FTP, HTTPs, etc. Also encompasses the letter and number codes used for identifying file types during transactions. 5010 is largely an attempt to standardize the file codes in a way that increases security through in-flight encryption with de-crypt at each end. This is only possible if there is a standard metadata set.

ICD-10 is completely different it is the International Classification of Diseases (Rev. 10). This is used mainly for e-billing purposes as part of the diagnostic reference. It is not the official standard in the US until 10/1/2013, HIPAA 5010 EDI standards is a prerequisite for use of ICD-10.

Device access Smartphones and tablet computers represent the next wave of technological innovation in healthcare not to mention medical devices and consumer health apps (see here for more thoughts).

Mobile is a key aspect of your long term success. Hospitals have a variety of high earning "part-time" and ad hoc employees with their own businesses to run. You need a way to integrate their independent process and access into your secure information systems.

As with any access decision the type of information that can be accesses has to be balanced against the need for audit and security. The key is to remember the needs of end-users:
Doctors need access to all data, so restricting parts of the records is not an option.
Nurses need to update records on the fly.

IoT devices- There has been a lot of new devices for use in healthcare- patient owned health apps, mobile phones and wireless medical devices (see here for more on this). One of the key short comings of today's EMR/EHR products is their lack of abilities on the user experience front. Hospitals need to move away from single point solution planning for applications to a information management strategy that includes integration of outside data- whether it comes from patients, partner clinics or device vendors.

IT managers need to take the initiative and do these three things:

1. Ensure that the process involves care providers and administration in the same room. These meetings cannot be for show. All decision makers must be involved. 
2. Get to know who the key decision-making doctors are in each department and develop a relationship. Some doctors are in favor of EHR find out who these are in your hospital/clinic and involve them in building a strategy for how to attack the implementation. 
3. Get care providers on-board during the demonstration phase. Take your key decision makers through the products ask questions about the mundane parts of the software (first impressions of the GUI, how to access the records) not just the big picture items.

Clinical data random information

I've become an information hoarder. As I spend more time thinking about Information Management and speeding the move to better technical systems, I am amazed how general the principals of design are between the different industries.

Here is a noobs (i.e. me) "plain spoken" understanding of a key term in managing patient data across hospitials and for predicative analytics and personal health decison making.

Level setting (i.e. in general the definition of Clinical data warehousing) Clinical data warehousing is a patient identifier organized, integrated, historically archived collection of data.

For the most part the purpose of CDW is as a database for hospitals and healthcare workers to analyze and make informed decisions on both individual patient care and forecasting where a hospital’s patient population is going to need greater care (i.e. patient’s are showing up as obese; therefore the need for specific hospital programs to fight diabetes are a good idea).

Data warehousing in healthcare also has use in preparing for both full ICD-10 and meaningful use implementation. For example; McKesson through its Enterprise intelligence module probably has plenty of CDW management capabilities the only interested in meeting the upcoming ICD-10 and meaningful use deadlines. These kinds of worries are only for US hospitals. However since Canada requires ICD-10 compliance for all EMR systems this does present a benefit to Canadian healthcare.

In principal since data warehousing at its core is about building a relational database and should be EMR supplier agnostic. Since McKesson is an ICD-10 and meaningful use- ready supplier, the database itself should conform to standards that would allow general solutions to be used. This article goes through some of the potential benefits and pain points. It is tailored to clinical trials but the underlying message that building a CDW is a ongoing procedure is the same for other uses.

One example of how this may be done is Stanford’s STRIDE; they used HL7 reference information model to combine their Cerner and Epic databases. This is part of a larger opensource project that may be an option if an organization has some development expertise.

https://clinicalinformatics.stanford.edu/research/stride.html

Since the main user of CDWs tends to be the people doing the analysis (current buzzwords for search for analytics include:BI, Predictive analytics, enterprise planning, etc) it is probably useful for Health IT professionals to understand its WHO and WHAT the CDW is for within the organization...i.e. have a full blown Information Governance plan that places a value on information not just a risk assessment. 

Security without usability isn't better healthcare

I spend a lot of my time understanding how information is stored, accessed and protected as part of my role as a IT analyst. I always am astounded at how little of what is standard practice in many industries as not filtered over to health care and/or life sciences (Pharma+Biotech+academia).

The recent hub-bub about ACA (AKA Obamacare) has completely yelled over the real transformation opportunity in healthcare. Up until the recent deadlines and political fights regarding ACA "everyone" was really concerned about meaningful use. The TL;DR version of the MU legislation is this: make information available to care providers and patients.

So what are we really talking about here? It is really pretty simple; it is information management and the processes that guard against mis-use while enabling productivity.

Lets be honest the EHR/EMR solutions implemented at most organizations do not enable productivity or protect information. Doctors hate them because they do not fit their work patterns (see here), hospitals are have significant issues with data protection (see here) and importantly it is not mitigating the biggest risk to patient outcomes (and hospital liability) (see here).  

It is time to re-think the information silos in healthcare.

So if a single poorly accessed EHR is not the answer, what is?

I would argue that we need to think about this based on information flow and how we expect the value to be delivered. In this case patient care.

An interesting model to think about is the Canadian delivery model. For example; Ontario E-health has determined it is neither cost effective nor timely to build a single system for every hospital.  At the moment, 70% of all physician practices and hospitals already have some sort of EHR system in place. So rip and replace is not an option, the reality is we need to make lemonade.

Since Ontario funds the hospitals through direct allocation of tax revenue, it is loathe flush that money down the drain. 

Therefore the best approach is to control the data itself (including digital images, prescription history, surgery, etc) and letting the individual hospitals control how they view and use the data. 

In other words- Make it easier to access information based on who you are and what you need the information for!

Focus on the Information exchange layer

Consolidated Information Management layout for Patient care focus. 

So how do we do this without moving to brand new systems and shiny new toys?

The same way every other industry is doing it; especially low margin high risk industries such as Oil and gas, Insurance and Manufacturing. Keep the clunky but very secure system and take advantage of the new technologies that enable information sharing. Instead of all-in-one solution add an ECM or portal to manage rights, search and presentation. It will be more cost-effective than doing nothing or rip and replace.

This structure controls movement and access to patient data, allowing for quick access to the appropriate information based on job and location.  It provides a structure that takes advantage of the current investment in a secure database yet provides a flexible layer that is designed to convey information in context for end users. 

This may not be the best system or the system that you would design from scratch with an unlimited budget, but it gives a long term flexibility AND doesn't require a rip and replace of your current EMR/EHR. It should provide very good, highly usable healthcare at a reasonable cost.

The way they are going about the change may not be splashy but it will work for both patients and doctors- that’s a great thing. The one thing it won’t fix is the doctors who refuse to use it-and that is a bad thing.

There is additional cost involved in this model but if teh doctors and nurses do not use what you have now.....would salvaging that investment be better?

Love any comments or critique of the model.

Big data is just a euphemism for lazy and cheap

Maybe I'm getting cantankerous but I'm really over all of the talk about big data and how it is going revolutionize the world businesses are going to so efficient they will only need a CEO and a lowly marketing guy. Governments will so efficient taxes will be almost unnecessary. 

Enough! The reality is that big data isn't new and most organizations are not mature enough or focused enough to take advantage of the new technology. 

Learn the lessons of the past.

I was (am) a scientist. I did my Ph.D in neuroscience and genetics back when sequencing a single gene took months. For reference, the bleeding edge technologies can deliver a whole genome (about 20 thousand genes) in 15 minutes. 

I have already complained about the challenges in knowledge management in science - and the parallelism in businesses today in this blog. I'll summarize; businesses suck at getting the right information to workers because they are cheap and lazy. 

No one wants to pay to do it right, everyone thinks that the app should be cheap and reduce labor cost by reducing the need to hire smart people. 

Well folks organizing and analyzing data/information is hard and takes a deep understanding of the difference between junk and INFORMATION.

The original Big data problem

Scientists have always generated large, complex data sets that are almost too difficult to comprehend.

As we enter the genomics era in science it has gotten worse because most scientists have not taken the time to do quality control on the information that they submit to public databases. The public data is very spotty at best; how many scientists can honestly say that they trust the gene ontology notes?

N.B. For non-scientists the Gene ontology database is a repository of notes, data, or published papers about our combined knowledge of each gene's function, interactions and chemical inhibitors. It contains links across species and across several databases.

The problem is that it is incomplete NLM/NIH does not have the money to maintain it-nor do any of the primary owners. The pace of growth is to much for the curators to keep up with. The number of different sources has also grown, you have images, gene expression studies, drug testing, protein interaction maps. 

Science has had a big data problem since before computers. How has the scientific community moved forward and had success even in the face of such poor data stewardship?

People.

Anyone how gets through a Ph.D has a great analytical mind. They can see through poor quality data to those nuggets of truth. How do they do this? They focus on finding an answer to a question, and then they build out from that question until they have built a complex multifaceted answer.

You wan to know why science is becoming stagnant and have serious ethical and just plain stupid errors of reproducibility?

We do not train scientists to be critical and form questions. We teach them to get a whole lot of data and mold it into a a beautiful story. The logic being that if you look at enough data the truth will come out. It never does; if you start out with biased data you will get a biased answer. The data sets are inherently flawed.

There is no big data only poorly framed questions. If you have a big data problem it is because you have been a poor data steward and you don't have a question. so you have no ability to start sifting through information.

Their has always been a lot of information it is just That we trained people to work with it, understand it, analyze it and make decisions. More importantly we understood that failure was a good thing, it is a chance to define the question and focus on things that will work.

A lesson not learned

There is no such thing as big data, just better storage of the vast amounts of information that life generates. Nothing has really changed it just the problem is more visible-and we downsized all of the keepers of the knowledge. Most organizations- healthcare and Pharma being the key culprits refuse to train people to think critically and scrutinize the veracity and quality of information/data.

You want to fix the big data problem? Train people to ask questions and let them answer the question. Or hire someone well trained already such as the overstocked "bioinformatics Ph.D" class of scientists. The biottom line is that new shiny system is still going to give you crap data if the person asking the question is can't ask good and insightful questions.

Realize that autocorrect is the state of the art in predictive analytics right?......let that sink in for a minute. Are you will to leave your career or company to this?

You don't need more data, you need the right data and the time and confidence to fully vet the quality of the data. We need people that understand today  to test how well that information fits with the world today. This is a key element of accurate predictions. 

In biomedical sciences this really comes down to how we train graduate students; do we make them learn statistics or just hope that excel is good enough? Are we willing to mentor students or are they just cheap labor for the gratification of the professor? Do we pay attention to how we store and mange information so that the next student can find it?

For most businesses it comes down to why? Is there a business question that we need to solve, what is the problem that we need fix, is there a new source of revenue that we can exploit? What are our past failures and what can we learn from them?