A frequent question I am asked by startups and their software focused leadership teams is, “how do we generate sales and what is the appropriate process to follow in creating our sales expectations.”  My friend Steve Carbonara has been selling software to healthcare enterprises for years so I asked him to write a companion to his piece on selling to hospitals. Steve is currently the Chief Sales Officer at Cohealo, Inc., a VC backed healthcare services firm that optimizes purchasing and consumption of medical devices and equipment. After 8 years in corporate sales with Misys Healthcare and Allscripts, Steve has worked as a consultant with many start ups and medium sized businesses to optimize their sales processes.  If you’re a tech-focused startup there’s a lot of help out there on the technical side but very little on the sales side so I invited Steve to share his advice on how to sell health IT products, especially EHRs, into physician practices and hospitals. Here’s what Steve had to say on improving sales through better funnel management:

In my last article in this series we discussed how to sell Electronic Healthcare Record (EHR) software into the medical community and matching the selling process to the buying process.  Today we’re going to focus on creating and managing that funnel of prospects; now that we’re matching up to the needs of our prospects, we’re going to be matching up to their time lines as well.  As I have worked with solutions including EHR’s, Personal Health Records (PHR), Business Analytics, Clinical Analytics, Practice Management Solutions (PMS), and other health information technology; this article is transferable across the HIT landscape.

So what is funnel management and how do you maximize your return on investment from your funnel ?  Basically, funnel management is learning how to manage your time and your expectations.  If you want to have success in this industry of healthcare information technology (HIT) you have to know what it is going to take to make the monthly, quarterly and annual revenue that your firm needs to stay afloat, or to thrive.  Some people think that if they sell product and their basic overhead is covered then they are successful, the problem with this assumption is that you have more than basic overhead.  Have you thought of marketing costs, commissions for sales people, channel partners revenue needs, reinvestment into the products you sell, incremental revenue growth, market share and market position?

So, to begin good funnel management, you need to make sure you have a good pricing scheme.  Pricing is the perceived value of your product as seen by customers based their needs.  Did you perform market research to validate your pricing assumptions?  Are you priced into the industry but not low enough to keep yourself from going out of business?  Are you priced too high and thus above the perceived value of your product?  Once you accomplish the goal of pricing yourself as a valuable product, not as a ‘bargain’ product, you will be able to sell within the needs of the industry and make money.  This is important as I have seen many companies in this industry try to ‘buy’ their way into the market but it ended up with them being the ‘cheap’ solution, a title you don’t want to win, in any business.

How do you create a value proposition for your product once you define price?  Through your market research you should speak with your target market, and learn from them.  If you listen, you will understand that they tell you how to sell them, they tell you how to price your product, and they will tell you what was too much and what was cheap.  If you listen then you will take their input and build your value propositions around their needs.  If you have built your value propositions around your needs instead of market needs, you will not be able to match your sales cycle to the customers buying cycle.  If you don’t even know what their needs are, take the time and make the investment, do your homework up front in market research and you will save money in the end.

Building Your Matrix to Success

So, pricing is in place, now we can build the matrix for success that will deliver us the revenue we need.  As the CXO of your company you need to look at this from the point of view of your sales person.  What does this person you hire need to do to meet your financial goals and earn the money that they need for their family?  Once you understand this matrix you will understand how many sales people you will need to hire to meet all revenue goals.  Let’s begin.

As the CEO you know your research informed you that your competitors have sales representatives closing an average of $500,000 a year in new business.  You learned that they have a typical deal size of $20,000 and that they sell 100 units per deal at $200 per unit.  Your basic sales plan lines up easy from here, each sales rep will have a quota of $300,000, you will look to average the same $20,000 per sale deal size with $200 per unit being the price of your widget.  This means the sales rep will sell 15 deals this year to hit their number, or just over 1 deal per month.

Why did I go at $300,000 and not $500,000?  Because you understood that your competition is a large national company that has been in business for 10 years with name brand recognition and you are new with less marketing spend and much less brand awareness.  I would be honest with myself and with my new sales team, letting them know that my expectations are that we are going to work together to build this company and that means we will have to fight for every deal and that you know that each of them will lose more than they win this first year.  You align yourself with your team, you build the trust and credibility that they need to have in you and in the end you set the appropriate expectations with yourself and your investors.

So, how many contacts do you need to make a week to begin building a funnel that will drive 15 new contracts and $500,000 in gross sales?  I would say 400 new contacts.  Why? Where to start:

1.  Because healthcare information technology as a market is saturated
2. Because the big vendors, like Epic, Allscripts, Cerner and EClinicalWorks have an in with the hospitals and clinics that you can’t match
3. Because competition will ‘buy’ a deal away from you
4. Because you’re not obtuse enough to think you can close every deal you’re in.

I would go with the knowledge that the typical direct to consumer marketing effort relates to a close rate of 2-3%, but even if  you give yourself credit for being better than that rate you still have to admit that your company may be small and not well known, and it doesn’t have a client base who can sell forward.  With all this being said put that number in front of yourself and plan your attack.

Recap: 400 new contacts at 3% close rate provides 12 new clients this year and at $20,000 per deal you will generate $240,000 with your knowledge that you’re better than average and you will pick up the other 3 additional new clients getting you to the $300,000 per sales rep. quota.

What activities define your week if you’re trying to work within 400 active deals?

Well, this depends on your marketing budget, and you have that handy because you finished it while you were performing your market research.  If you have an appropriate budget then here is an example:

These activities are what each sales rep will do to achieve their goals and each activity builds upon the one prior to it, thus reflecting the sales cycle.  Each widget needs to be sold a little differently, so these steps are easily changed to reflect the appropriate steps that your firm demands of your sales team.

The following activities are reflective of your company performing activities that help drive market and brand awareness to help your sales team reach their goals.  These activities may be performed by the sales team, the senior leadership or a marketing team; but in the end, they are not part of the sales cycle, but are part of your overall marketing plan, helping sales generate qualified leads

There’s a lot to do and being engaged in 400 active deals is not easy, time management is not a gift, it’s a talent that you have to develop and then manage.  When working with my clients I have put together matrixes exhibiting exactly how much time a sales rep, and thus the company, should focus on each of these activities and what value should be derived from each.  Defining how a sales rep should spend their 8 hours a day working for your firm can never go to far, as it sets expectations and defines success and failure so that no one is surprised at year’s end, with failure or with success.  Go as far with this exercise as you want, the more detail documented and the more you can hold yourself accountable to your daily, weekly and monthly totals, the better off your firm and your sales representatives will be moving forward.

Join me for my next posting where we will dive deeper into managing the funnel that you just laid the groundwork to build.

Following HiMSS13 in New Orleans I sat down last month in a BlogTalkRadio broadcast with Dr. Pat Salber (@DocWeighsIn @HealthTechHatch), Gregg Masters (@2healthguru @ACOwatch) and John Lynn (@techguy) with a ‘debrief’ of our key HIMSS13 take-aways as well as our latest venture, Influential Networks.

I covered the following topics in the podcast:

• The HIMSS 13 cheerleading and “echo chamber”
• Are we moving faster with MU than the industry can really accomodate?
• How MU is creating false demand and a false market and when we might be able to move back to real innovation
• Supply of  health IT professionals and whether they’re meeting the demand
• Medical device connectivity
• Population management
• Lack of evidence-driven selection of technology

I’ve said repeatedly that any cloud / SaaS vendor that wants to be taken seriously in healthcare must be willing to sign a HIPAA Business Associate Agreement (BAA) and I was happy to hear that Box.com is now willing to do so. I’m quite pleased that we’re finally seeing some serious healthcare SaaS offerings from horizontal (non-healthcare-specific) vendors. Only when we move beyond healthcare-specific offerings will we be able to unshackle ourselves from the decades old legacy health IT vendors and that’s great news. While Box.com is only one vendor I think they will be the first of many general industry SaaS providers that will provide secure file sharing, mobile access, document management, and other important collaboration services that incumbent health IT vendors can now build on instead of having to reinvent the proverbial infrastructure wheel.

Last week the Box.com healthcare team invited me to participate in their “Secure Cloud Collaboration in Healthcare” webinar. The full event, audio, and screencast is available on their BrightTalk.com channel.

My point to the audience was that healthcare professionals are very resourceful and if IT doesn’t provide them the proper solutions they will not just wait for progress, they’ll take matters into their hands — creating a growing “Shadow IT” problem. In the webinar I talked about “Shadow IT” and how solutions like Box.com can reduce the problems of end users choosing consumer-grade cloud solutions that are not HIPAA compliant or secure enough for enterprise use.

Last week the Greater Chicago Chapter of HIMSS invited me to participate in their healthcare technology webinar series. I covered the topic “What’s Next for Healthcare Information Technology Innovation?” and the screencast with audio has been posted here. I covered numerous topics that are helpful for entrepreneurs and engineers that want to create innovative healthcare technology.

I posted an article recently concerning the need to be more practical in the use of data vs. the need to go after the latest buzzwords, i.e. Big Data. Dan Reber posted a great comment on the article that I found enlightening so I reached out to him to expand on his thinking. Dan is in charge of product strategy at Origin Healthcare Solutions for their Business and Clinical Intelligence application (Precision.BI). Given that Dan’s been doing data warehousing and BI in some of the largest university-based medical groups in the country as well as having trained many users on the design of their reports I thought he’d be a great contributor. Here is what Dan had to say about how to crawl, walk, and then run towards data analysis in healthcare:

I recently watched a TED Talks video by Matt Ridley that discussed how present-day ideas evolve from past ideas and how these ideas often combine to form new ones. The video revealed how it takes roughly a million people to create one modest computer mouse – from the engineers and designers, to the oil rig workers who contribute to plastic manufacturing, to the fabricators who put it all together, to the coffee producers who supply the energy for the said engineers, designers, oil rig workers, fabricators, and so on and so on.

This video was an eye-opening example of how technology revolutions are accomplished – through the culmination of ideas.

Healthcare organizations (HCOs) have only recently begun embracing the notion of analyzing the data they’ve been entering into billing and clinical systems for years. They’re doing this using tried and true products and processes that have evolved over the years within other industries. But there’s a problem with this, those products and processes rely on ideas that came before it and had been applied in those industries. In other words, healthcare is starting on step 10 before completing steps 1 through 9.

Technology in healthcare is like the platypus – a piecing together of ideas that have not evolved properly.

I believe there’s a need for evolution in healthcare technology, data analysis in particular, before the latest buzzwords can be implemented. As Shahid has said previously, we need to crawl, walk, and then run. Here are a few significant steps I believe are required for healthcare data analysis to significantly evolve from crawling to walking:

Exception Reports – It’s essential that every organization have standard financial reports to analyze the status of its billing and receivables. These reports are pretty straight forward. However, clinical reporting and quality measures are relatively new and have various requirements that call for strict adherence to procedures to be certain of accuracy.

• To ensure procedures are followed, exception reports must be created to catch when these procedures are not followed.
• The most valuable exception reports must capture the “old way of doing things,” to make sure they remain “the old way.”
• When procedures are changed, new exception reports must be created.

Analytics Department Setup – Many HCOs have their IT department handle all reporting needs. This may work on the financial side (although I advise against it) but it will not work for clinical reporting and quality measures, as the domain knowledge just isn’t there.

Below are three professional positions I believe are essential for a successful, and more importantly accurate, analytics department:

• Implementation Champion – This position is responsible for the overall direction and vision of the implementation. May oversee projects that have high visibility throughout the organization. The individual is usually the CMO/CMIO or Medical Director and must have the ability to change clinical procedures within the organization. This position can be broken down into Quality Champion and Process Champion
• Implementation Director – This position is responsible for the day-to-day reporting and operations of the analytics department. The most efficient departments I’ve seen are those with a physician or RN, or both, to lead the operations
• Business Analyst – This position is responsible for the prioritization of requests and management of larger projects. The individual best-suited for this position is an SME of the EHR, and is usually part of the EHR implementation team.

Data Quality – I have seen many organizations disseminate reports before the data has even been validated. I am an avowed data geek and so I insist that data quality be top priority for any analytics department. Additionally, exception reports must not be confused with data validation. Exception reports do not take the place of validating the data, they simply show when a process is not being followed.

Below are a few important steps to follow for better quality data:

• Data Comparison – Create an automated process that compares the data in canned reports in the host system, to data within the data warehouse. Only send alerts when the data does not match. Don’t be surprised, though, if the canned reports are incorrect. We have proven many to be inaccurate.
• Validation Contests – At the beginning of most analytics implementations, many users will say that the data is inaccurate or just doesn’t look right. Why not implement a contest to see who can find the most issues with the data and even offer a bonus to the top three. This will do two things – help locate data issues (and there will be some) and help the users trust the data once said issues are resolved.
• Spot Checks - Always, always, spot check on each and every report prior to sending out for the first time. Then continue with periodic spot checks on random reports going forward.

Data analysis in healthcare is challenging and when done incorrectly it will be inaccurate. My years of experience within the industry have taught me that the above recommendations will provide a simpler process with – more accurate results, trusted information, and most importantly, will result in better quality patient care.

Healthcare technology needs to first evolve and adapt so that the “big data” revolution can begin.

I have been speaking and writing often these days about how single sign on (SSO) technologies are probably one of the most important components of health IT data integration. To help figure out how to integrate multiple systems using standards-based SSO approaches I reached out to Shahid Qadri, a Data Scientist and Software Developer for Applied informatics Inc. Qadri works on health data integration and semantic web and when I heard that he created a solution (which won second place) for an ONC single sign on challenge I thought he’d be the perfect engineer to help the rest of us. Here’s what Qadri had to say about WebID:

The Simple Sign-on challenge sponsored by the ONC through the Health 2.0 challenge was an exciting opportunity for us to learn about a sophisticated technology protocol and then being able to hack several open source system to implement a single sign on solution based on the protocol. This was a challenge that was truly a “challenge” for me, but an exciting and rewarding one (our solution was the second place winner!).

The challenge involved using the W3C WebID protocol to enable a single sign on across different systems used by the HealthData.gov Platform (HDP). The eventual goal of the HDP is to allow various administrators, contributors and even machines to be authenticated and authorized to access different open source systems. In a nutshell, our solution, OneLogin creates a “wrapper” over each of the systems (Drupal, Ontowiki, Virtuoso, Tomcat) that programmatically creates users within these systems with a given role and associates a given WebID with these users. Each tool’s wrapper is independent of the system and can be configured across different machines. The source code for OneLogin is available on GitHub.

In rest of the blog post, I will describe the background and technical details of the solution.

WebID is a W3C open standard for identity and password-less login on the Web. WebID is designed to alleviate the difficulty (and pain) of remembering different logins, passwords and settings for different websites. WebID in itself is essentially a URL pointing to a description of yourself in FOAF format. FOAF stands for Friend Of A Friend and is essentially an RDF vocabulary which allows you to describe your social web) combined with a self-signed X.509 certificate. X.509 certificates (X-men like sounding terms) are the certifications used to verify the identity of web servers via the SSL protocol. It is a secure authentication protocol utilizing FOAF profile information as well as the SSL security layer available in virtually all modern web browsers. Operationally, once you have a WebID with private key stored in your browser, logging into a website is as simple as selecting your WebID and clicking "log in”. Additionally, there are other benefits of creating a WebID. Other people can to reference you and declare social relations on the web (such as that you are their friend, colleague, parent, etc.) even when their profile is hosted on a different web server than yours. Thus the WebID can be a trusted and verified way to enable the Social Web, i.e., social networks between individuals, citizens, companies, universities, governments, while allowing each player to remain in control of their data they publish.

How Does WebID Work?
As mentioned before, the WebID is a URL, which points to a FOAF file. Now if you want to log into some site you simply provide the WebID which means to select a certificate from a list in your web browser. The server will then fetch the FOAF, extract the

certificate’s public key from it, and then ask you to prove your identity. Since you are the only one having the private key of the certificate that is easily done. And that’s it. From a high level point of view it is very simple, but getting to the nuts and bolts of it can be a challenge.

(Source: http://www.w3.org/wiki/WebID)

Is WebID Really Secure?
In order to secure and protect your identity two things need to be made ensured:

1. The FOAF file that your WebID URL points to should be under your control or that of a trustworthy entity, and

2. Make sure nobody steals your private key! Though, if you do lose your private key, disabling the WebID is as easy as removing the public key from your FOAF profile. Importantly, and this is a useful mechanism of decoupling the public key from the WebID url, is that replacing your public key certificate with a new one will never invalidate your WebID since it stays as a permanent identifier for yourself in the semantic web, independent of the certificate.

Building the Single Sign-on OneLogin Application

First we started with Drupal and added a module in Drupal for providing WebID authentication. But configuring that got a bit tricky as we got few errors while using the module. For example, after digging into the module code we found there was a bug in the import statements. After resolving this we wrote code to automatically create a user in Drupal with the specified role who can use the his/her specified WebID to log in to Drupal.

WebID Provider

Next step involved setting up our own WebID provider (the service that generates a WebID). To do this, we chose to use the open source system, Virtuoso (an enterprise grade RDF store) that we installed on one of our servers over SSL/https. It generated WebID correctly, however, the WebIDs generated from it did not pass verification test. After close inspection we found that although the certificate was generated correctly, it was not getting the FOAF data in RDF format. So we installed an RDF mapper module to fix this and then we were able to generate WebIDs correctly.

Next we worked within Virtuoso to associate its internal users with a WebID, as it had a built-in support for WebIDs. We needed to write a wrapper that can programmatically create an account in Virtuoso and associate a WebID. So we developed a wrapper in PHP and ISQL for the same (the code can be viewed here)

After configuring Virtuoso, we installed OntoWiki. We found that this platform also supports WebID authentication though it had been disabled in the current version in favor of OpenID. So we first got it enabled. Next we wrote the code to create a user and associate a WebID to it. The WebID user creation depends on the external plugin Erfurt in OntoWiki. After solving dependencies and other things we got our code working.

Next and last was the associating WebID with Tomcat/Solr. After basic search we found that there is a library for providing WebID authentication to Tomcat/Solr. This library was not easy to use as we needed lot changes in installation and as well as configuring this library with Tomcat/Solr, but after rigorous efforts we were able to configure it correctly (more technical details are on our blog).

In order to build the code to programmatically create users and associating a WebID we had to tweak the tomcat-users.rdf file. This is where the user list and WebID are stored as nodes. We used a PHP XML parser to append users to the RDF file (view code) .

Finally, the application was build that created users and associated a role in each system and used WebID to login to each system. Included below is a screen shot of the Application with a link to the GitHub repository of the application. A demo video is also available here.

Screenshot of the OneLogin Single Sign on System

It’s a common misconception that if executives at hospitals or practices don’t have time to deliver sophisticated IT solutions to their users that users will just wait patiently and hope that solutions will arrive someday. However, there is a larger Shadow IT movement in many clinical settings than senior executives are willing to admit. Given the wealth of cloud offerings available, many of which have better security in the cloud than some on-premises “clinical” solutions, Shadow IT is growing and will cause more problems in the future as we try to reign it in. Just as users found ways to use Microsoft Excel and Access to get around EHR database problems and financial system woes, the cloud is creating another generation of Shadow IT problems.

Sophisticated requirements like clinical collaboration, secure file sharing, medical grade document management, and secure messaging (especially texting) can not be easily deployed within a single facility because the default security mechanisms and firewall controls prevent their effective use. While senior IT folks try and figure out how to procure and secure their own solutions, users end up using Dropbox or other insecure solutions because it doesn’t require much to download and they can start sharing files immediately. Dropbox has such a high value that may senior executives may just look the other way because they know their IT staff can’t deliver anything better sooner.

Instead of being silent on easy  file sharing, announce that you will allow certain “good enough” secure cloud services such as independents like Box (my preference) or Microsoft’s Skydrive (which I like, too). Encourage your staff to bring you ideas for how they’re using cloud services and offer them secure alternatives so that Shadow IT doesn’t grow any bigger.

If you’re in the DC area on April 17th, join me from 11:30am to 12:45am at the BoxWorld Tour where I’ll be speaking on a panel with other experts on how the government is using secure file sharing options like Box.com. If Box.com meets the government’s rules for security and privacy, HIPAA is not that hard to accommodate.  so you should give it a shot. Join me at the panel to learn more and ask questions about how you can arrest your Shadow IT growth.

Many readers write to me regularly to ask what I think about “Big Data” in healthcare. I tell them that Big Data in our field is generally more hype than reality right now but that there’s a lot of promise and opportunity. To help elaborate on why this might be the case I’ve asked my friend Naeem Hashmi, Chief Research Officer at Information Frameworks, to give us his thoughts. Naeem has written a number of books on the subject of informatics and analytics and been on the front lines of engineering large scale healthcare systems to generate data for clinical analytical purposes. Here’s what Naeem had to say about Big Data in Healthcare:

The Big Data bubble in the Healthcare is just filled with the Hot Air – at least for now. Every one is talking about it but when you dig a bit deep with a pointed question, very quickly you discover that it has nothing much to do with the Big Data. We need to understand the spectrum of healthcare segments and look at each segment on feasibility of Big Data components.

I identify the following six distinct healthcare segments as:

1. Life Sciences
2. Pharma
3. Med Device Manufacturers
4. Care Provider
5. Payers
6. Public Health

I did not include Patients in the list as they are mostly content generators, consumers and have much to benefit from but they are on the mercy of other segments. Life Sciences and Pharma segments are already exploiting Big Data elements for OMICs in discovering new drugs and therapies and will discuss details in the later sections.

In this article, I will focus on realities of the Big Data in the Care Providers segments only.

To frame the discussion, let me describe what I mean by Big Data. The Big data consists of the following elements:

• Content Attributes: 4 Vs (Volume, Variety, Velocity, Veracity) – widely known.
• Technology Stack: Hadoop-like stacks and Analytics Appliances
• Computing Model: Extremely Reliable Parallelism
• Query Model: Hadoop/Map-Reduce use search method (not complex query). So if the intent is to do searches against large unstructured content, the pure Hadoop/map-reduce functions are good fit. Not SQL.
• Connecting the Dots: Understanding the associative meanings of the interesting patterns hidden underneath massive content-stores.

Care Providers:

The Healthcare Provider segment is extremely fragmented. With ongoing Healthcare reforms, there is some hope that use of EHRs may result in some meaningful use of clinical content. Most of the CIOs are too busy in addressing ICD-10 and CMS Meaningful Use 2 mandates and very few have the time or energy to invest in true Big Data platforms. Data management still remains on the Legacy platforms. So how /where does Big Data fit in the Care Providers segment and challenges?

Data Jail

EHR vendors have a lock on patient clinical content in their own proprietary ‘Data Jails’. In my many years as a practitioner I have found it quite difficult to access clinical content and analyze it within these proprietary systems so the Volume and Velocity needed for Big Data is not easy to obtain.

I am using the term ‘Data Jail’ because it takes me back to the early 90′s, when ‘data jail’ term was widely used for the ERP vendors. Then, IT folks had hard time to pull data out of ERPs for reporting and analytics. Today, ERP vendors are quite open and allow accesses to the data. EHR vendors need to learn lessons from ERPs for openness, interoperability and scalability. Opening access to their repositories is good for their customers and for Big Data.

Complexity, Low Volumes and Static Data

Most clinical data volumes are quite low and static because you cannot change any historical data in a clinical record. Even though you may append a new record, the rate of change is fairly low unless you’re talking about genetic or proteomic data (which is rare in most settings). Moreover, Clinical content is quite complex. Here content context and semantics are more important than the volume and velocity so you can live with standard relational databases or analytics appliances without the Big Data tools like Hadoop.

Real-Time Patient Monitoring

Real-time patient monitoring applications such as bedside heart monitors, OR monitors for anesthesia, dialysis patient monitors, etc. are being piloted in care facilities that demand use of Big Data platform to quickly consume real-time events and signal possible life threatening adverse events before occurring. Increasing use of mobile personal health monitoring devices that stream data back to the care providers also require Big Data elements to capture and predict possible health issues and alarm the care takers and patient for actions. Good candidate for Big Data driven applications.

ACO Level Analytics

ACO level data volumes will be quite high and Big Data will play a key role to develop analytics. The challenge will be how to pull detailed clinical EHRs for the ACO and then to harmonize content in the ACO hub to ensure content quality. Lack of data exchange standards in practical use remains a problem. Even when vendors conform to patient data exchange standard, such as CCD, the actual implementation varies significantly and makes harmonization difficult. For example, Continuity of Care Document (CCD) implementation of two vendors could result in different patient clinical content. That’s because one vendor could provide latitudinal CCD while the other provided encounter specific CCD. Both conform to the CCD specs but payload was different. Here, for the ACO Big Data repository, you need to build a high performance harmonization engine to ensure content consistency before you embark on Analytics.

Valuing Big Data

After going through pain of Big Data implementations, the end goal should remain how to bring valuable insight back in the clinical processes – embedded within the clinical workflows. Most people think that just displaying charts and graphs on a stand-alone dashboard will do the job but of course that’s not the case. Physicians and clinicians do not have time to hop through many screens to care their patients. We need to embed such findings within the clinical work-flows so they have the right alert at the right time for the right patient. You may be stuck with an EHR’s limitations on how open they are so you may not be able to embed non-EHR event with their clinical workflows or work-lists.

Emerging Big Data Opportunity – Comparative Effectiveness Research

As social animals humans they socialize and chat about their daily life stories including dealing with their health issues, families, care givers, behaviors and medications. Such social communication is a gold mine to Pharma and Clinical Outcome researchers to understand effectiveness of a specific therapy. This new field is called the Comparative Effectiveness Research (CER) and Patient Centered Outcomes Research (PCOR). Through the use of Big Data analytics, CER will significantly improve patient care through development of personalized therapies.

Emerging Big Data Clinical Analytics providers

Last year, I served as a judge for the Massachusetts First Health Datapalooza competition. Several Big Data driven clinical applications were part of this competition. I had good discussions with a few vendors. Though their solutions are unique and valuable, none have thought well on how their solution will fit in the clinical setting, client EHR workflows or leverage client’s clinical platform. Just about all Big Data analytics providers are start-ups coming up with innovative care solutions.

When I see in the Big Data market place, it reminds me of ‘dot com’ era of the early 90′s. Lot of small innovative groups developing fascinating tools just like todays in the Healthcare world and unfortunately, a few will survive. My suggestion to such innovators is to keep in mind to develop their Big Data solution are interoperable with the EHRs or ACO business/clinical applications.

Closing thoughts

Big Data in the Clinical practice is just in its evolutionary stages. Most CIOs may be thinking about Big Data but to best of my knowledge I have not seen a pure Big Data implementation in any hospital or clinical practice. Only a handful of large global network of commercial Clinics have implemented such Big Data platforms in production.

Building a Big Data platform and having a team of skilled data scientists in a Hospital or Practice setting is not easy. It is Expensive as well. You will notice that today, only University Hospitals are starting to explore Big Data in the healthcare setting because of funded academic research programs and available pool of graduate students ready to investigate unexplored content. Same is true for the small start-ups companies to develop key healthcare analytics using the social data for public health and patient engagement applications. Today we are just taking baby steps — and we have a long road ahead of us to exploit the true value of Big Data in clinical settings.

Any thoughts or comments?

One of the key tenets of both the HITECH and Affordable Care Acts has been to drive improved patient care and reduction in cost by applying technology across all healthcare entities. A bigger challenge is how do to make multiple technology purchases interoperate within a provider network and / or across provider networks.  There are solutions out there that can make it happen, but to make sure interoperability happens consistently, testing technology integration touch points is crucial but not easy without the right test infrastructure. I reached out to Michael Brown, a senior Health IT specialist at to AEGIS.net which serves numerous government customers, to get some advice.  He has helped many agencies as a testing consultant for several years, setting up and deploying an infrastructure for interagency information exchange so I asked him to give us some advice about what methods will ensure that interoperability is maintained amongst multiple systems throughout the software development lifecycle.  Here’s what Michael had to say:

A primary element within communication, especially in the domain of Health IT is the timeliness of conveying an idea or fact while it can be used in a meaningful way. In the case of the latter example, information will need to be transferred and understood between two healthcare organizations, leading both to be able to share information, or interoperate, regarding the patient’s medical history in a timely fashion over a network. Effectively sharing this information promptly directly impacts the quality of care provided to the patient and affects the outcome of the medical episode (i.e. the emergency surgery).

The systems which facilitate interoperability do so by connecting multiple Electronic Health Record (EHR) systems through the use of Application Program Interfaces (APIs) and web services. To accelerate early adoption of EHR systems, the Center for Medicaid and Medicare (CMS) in close collaboration with the Office of the National Coordinator (ONC) implemented a set of standards and rules regarding the adaption of EHRs, deemed Meaningful Use. Many implementations of EHRs contain proprietary language and formats which are translated within APIs and transported using web service standards. The proprietary message must be converted into a standardized format readable by any EHR system. Large amounts of resources are dedicated to developing, testing, and verifying the secure exchange of healthcare information.

A method to ensure interoperability is maintained amongst multiple systems throughout the software development lifecycle is through the idea of testing early and often. This process infuses quality into the Healthcare system and limits the amount of errors encountered as a system is moved into a production environment. Open source tools, such as the Developers Integration Lab (DIL) provide a method to ensure quality throughout the development lifecycle and ensure interoperability as a system is on boarded into a production environment.

The Pieces of Bi-Directional Health Information Exchange

Referring to the above figure, an EHR vendor directly connects to an adapter, and the adapter to the gateway. The adapter is the component which transforms the EHR specific message into a standards based message. The message is then transported at the gateway layer to another gateway on another node, picked up, and processed within another EHR’s adapter. These gateways contain endpoints which are used to send various types of web service calls. This type of architecture is most often found within a bi-directional health information exchange, or BHIE. There are other architectures. For example, a messaging architecture known as Direct utilizes secure email to transfer healthcare information between providers.

The transformed message within the adapter is sent out in a standard format using Simple Object Access Protocol, or SOAP message relying on Extensible Markup Language (XML) to provide structure to the message. The SOAP message invokes a specific web service and is transported from one valid gateway to another. The message can be in either the form of a request message (seeking specific information in another health system) or a response message (responding to a request from another system to provide specific information). The SOAP message transferred between the endpoints may contain security headers to ensure only a specific endpoint can understand the message.

Testing Interoperability

Many systems currently onboarding at the Federal level (eHealth Exchange) or State level (various State Health Information Exchanges) utilize variations of the described architecture. Testing this architecture, the message structure, and the various system APIs is a complex undertaking requiring a robust test harness.

Upgrading a component within a healthcare system may directly impact interoperability among multiple healthcare systems. As systems are updated within an organization’s ecosystem, testing must occur in a test environment which accurately simulates production use. As displayed in the above figure, a break in the connection between the Patient Database and the Health System, possibly due to a server upgrade, has severely impacted the usability of the system. The Health System can no longer exchange requested patient information with outside vendors. The figure demonstrates how upgrading a Patient Database may impact various other components and create interoperability challenges.

Recently, an open source test harness has been created for providing a method for developers and testers to actively test interoperability throughout the development life cycle. The Developers Integration Lab (DIL), an open source tool provides a large volume of Synthetic Patient information which allows a user to register endpoints and begin testing against multiple hosted systems in a simulated production environment. A general overview of the Open Source testing tool is provided here: https://www.youtube.com/watch?v=gHVwpxpVwbQ.

Interoperability is a fragile yet necessary component of the modern healthcare system. Patients rely on interoperability among multiple systems and organizations to properly collect and receive benefits, payouts, and overall improved quality of care. Testing interoperability must be completed in an iterative, agile fashion; ensuring bugs are found and repaired prior to a production release. Through the use of iterative, open source testing tools for healthcare interoperability, systems may maintain a high level of interoperability providing a method for healthcare providers to guarantee an increased level of care to patients, the creation of the longitudinal health record, and meeting the standards highlighted within Meaningful Use Stage 2 and beyond.

The popularity of blogs has been a given for years now but now that experienced professionals have tools like LinkedIn Groups, Google+, and even Facebook I often get asked for tips on what kind of content/information is “best” to be shared. I don’t know that I have any definitive answers but here are some suggestions based on the kinds of content I share with my readers:

• Focus on actionable advice and talk to people as if they’re smart enough to act on what you’re suggesting. News and views can be obtained from anywhere and almost everyone knows how to report facts — the question is what to do with the facts available and how to turn what you know into action.
• Be provocative, novel, and unique with your advice or perspective. Nobody cares if you agree with everyone out there unless you’ve got something to add to the civil discourse.
• If you want to be truly different, offer evidence-driven or evidence-supported information on your blog. Given the amount of pseudo-science and junk out on the Internet, if you can support your opinions with actual facts and evidence you’ll differentiate yourself quickly.
• Share solutions to common problems — you may be an expert but there are others that may be newbies in any particular field. If you know how to do something well, write about it in micro-article formats and share it with others even if the activity seems mundane or obvious.

What would you advise to others?