When I think of the boom in Healthcare IT, I am reminded of the multitude of systems which allow providers to communicate to other providers, patients, and insurance companies to facilitate the care of the patient and management of health services.  I know there are several websites available to communicate with the patient or member community, yet, the mobile applications are generally lacking. While there is an app for your health and fitness, there aren’t many apps for the patient which insurance companies or health care providers are supplying.

Most project managers have experienced projects with too few resources or a staff with insufficient skillsets. Specifically within the health care industry, the skillsets are in short supply due to the complex nature of health care; the ideal business analyst may be a nurse with an IT background. There is a sundry of knowledge, code sets, processes, jargon, and vendor systems that can differentiate one resource from another.  How do you overcome all of that and find the ideal candidate that can hit the ground running? Developing a creative resourcing approach is usually a part of any project manager's role when resources are scarce.

1)      Hire resources with a very strong IT background who will build their own knowledge about your environment and health care. Any health care background may be acceptable, even though it isn’t directly within the same area.

It is really bad, according to a recent survey by the Ponemon Institute (available here with registration). The white paper, entitled Health Data at Risk in Development: A Call for Data Masking, presents the results of a survey of 492 health care IT professionals on their companies’ practices regarding use of live personal health care data in application testing.

It makes a scary read.  Here are the lowlights:

Who would want to be a national health care administrator?  Who would want the responsibility for managing health care and formulating health policy for tens or hundreds of millions of people?  It seems obvious that such decisions would rely on quality data.  A recent interview impressed upon me how much data managers can learn from a field where data recording millions of separate life and death decisions aggregates to support decisions on the future allocation of health care resources.

Heather Richards of the Canadian Institute for Health Information (CIHI) was recently interviewed by the Australian magazine Image and Data Manager on CIHI efforts to provide neutral, objective and unbiased information to those making health care allocation policy decisions. Ms. Richards also happens to be Director of Publicity for the International Association for Information and Data Quality (IAIDQ).

In a detailed, concise, and refreshingly buzzword-free conversation, Ms. Richards described CIHI’s approach to improving data quality.  To me, that approach boils down to these three themes:

Margaret, who was an average sales person, moved from Washington, DC to Richmond, VA, whose market is one fifth the size, during the month of June.  When the annual evaluations of sales performance were done in the month of December, she was listed as the top performer in the Richmond market resulting in the company promoting her to Sales Director.  The next two highest ranked Richmond salespeople had been the consistent leaders for the last several years and outperformed Margaret since she arrived in Richmond.  Her very high sales numbers during the first six months of the year skewed her average, placing her above the rest of the Richmond area.  In this example, if the decision makers had correct information handy, and used it appropriately, would they have promoted Margaret over her new Richmond peers?

Here is another example.

The Atlantic, not typically a technical rag, recently presented an article by business and economics editor Megan McArdle on health care data integration entitled “Paging Dr. Luddite”. The article brings to a mass audience an understanding of both the importance and difficulty of data integration, but the title and general anti-healthcare-professional tone seem counterproductive.

The article opens with a compelling example of data silos, detailing the challenges of integrating the many data feeds from sensors attached to a premature baby “as small as the hand that cradles” it. Each sensor produces a data stream, and, in a field trial at Toronto’s Hospital for Sick Children, IBM is integrating that data in near real time to identify early signs of infection.

I've worked with health care data for the past few years, and in a recent conversation I realized it might be valuable to detail some of the complexities of health care data for those who might enter this growing field.  Of course these considerations aren't unique to health care, but they are typical of the challenges that the new health care application developer or analyst might face.