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Originally published julio 17, 2007
To understand the future direction of healthcare business intelligence, one first needs to look at three things:
The first area (i.e., business and policy issues) is essential because today’s business problems become tomorrow’s business analysis applications. The second (i.e., emerging business intelligence capabilities) is also important to understand because while they might be novel today, they will become part of the normal, everyday toolkit tomorrow. And finally, today’s overlooked applications will one day appear (seemingly overnight) as the hot new areas for business intelligence applications in the future.
This article will examine each of these three topics in depth and then bring them together for a look at where healthcare business intelligence is heading.
The list of hot topics in healthcare is well known both inside and outside the industry. It is in the news constantly, discussed at the water cooler and across the backyard fence, and forms a major portion of the political platforms for both sides in the upcoming presidential election. Priority issues in healthcare include:
In reality, most of these issues are not new. Consider this list of historical efforts to tackle these problems:
- Flexner Report (1906) focused on establishing medical education quality standards.
- Lord Dawson Report (1920) focused on the most effective structure of the medical profession to improve patient care.
- Goldmark Report (1923) focused on closing gaps in nursing care.
More recently, there has been a deluge of studies from the Institute of Medicine (IOM), including:
- To Err is Human (2000) focusing on patient safety gaps.
- Crossing the Quality Chasm (2001) focusing on a broader set of quality aims for the 21st century.
Healthcare Cost Control
- Committee on Costs of Medical Care (1932) summarizing the imbalance between healthcare delivered and the cost of that healthcare.
- Crisis in American Medicine (1968) from the British medical journal, The Lancet, which spoke of rising costs and falling performance.
- Research and Policy Committee (1973) conclusions regarding closing the gaps in financing healthcare.
- Pepper Commission (1990) proposals for healthcare reform (i.e., financial reform).
The concept of national health insurance may seem like a fairly new development, but consider this...
- Bull Moose Party Platform (1912) included national health insurance as one of its planks. National health insurance has come into and gone out of style at various times since then.
So there is little that is really new among the “hottest” issues in healthcare today. What is new regarding these issues is the pervasiveness of data and information about these issues that exists today versus what existed in the past. This wealth of information is a double-edged sword for healthcare providers as they attempt to solve these problems.
On the one hand, healthcare consumers have more information available to them, which can be used as a weapon against the provider. Purchasers have more data about healthcare providers today, which increases the pressure during negotiations. Payers have similar amounts and types of data such as claims histories, quality information, demand data and litigation histories. Even patients have gotten into the act of data-based negotiation using the vast stores of information on the Internet.
On the other hand, having more data available to all participants in the healthcare equation means that, as a society, we may finally be able to solve some of the most pressing issues that have plagued the healthcare industry for so long.
In healthcare, there are two broad types of analytical capabilities that are important for this discussion. The first involves healthcare-specific intelligence and decision support capabilities. The second includes general business intelligence capabilities that can be applied to any organization in any industry.
For the most part, healthcare-specific analytical capabilities have been built into other core operational applications as well as embedded in medical equipment and devices. Seldom have they been successfully put forth as stand-alone intelligence applications. For example, significant intelligence is built into CPOE (computerized provider order entry) systems, CDS (clinical decision support) applications, telemedicine devices (e.g., remote vitals sensing appliances) and handheld computing tablets seen everywhere in hospitals, long-term care facilities, clinics and even in the school nurse offices. While the central function of these technologies is not analysis, they all employ analysis to make them more valuable.
Stand-alone analytical applications, most notably expert medical systems, have not always fared as well for a variety of reasons. The chief impediments to applications whose primary mission is clinical analysis and decision-making stem from allowing a machine to decide the best course of treatment for human patients. Key issues include liability for the doctor (or other healthcare provider) from following the machine-generated advice (what if the system is wrong?) or from not following the machine-generated advice (what if the system is right and I just ignored it?). Suffice it to say that healthcare-specific analytical applications have been most successful when embedded into other medical and nursing applications, equipment and devices.
Contrast the deep intelligence capabilities within clinical applications to the situation currently existing within the services delivery and administrative sides of healthcare organizations. Operational, financial, purchasing, asset management, patient information and staffing and scheduling systems have been starved in terms of investment in intelligence capabilities. Most are operating in the information “stone age” compared to other industries such as retail, manufacturing, insurance and banking, despite the fact that they are organizationally just as complex as these other industries, and often even more complex.
Virtually every industry is ahead of healthcare in the use of proven, basic business intelligence tools, processes and technologies. This is where the current political and economic battle is being fought, and this is where general business intelligence capabilities can come into play with the greatest effectiveness. These business intelligence technologies include:
Widening the gap even further is the fact that organizations in these other industries are not standing still. They are moving beyond the basics and embracing capabilities that allow them to run faster, smarter and smoother operations. These capabilities include:
For healthcare organizations that are taking a daily beating in the press for lagging in the adoption of information technology, the situation is somewhat akin to trying to catch a flight that just left, only to find that the next available plane is already taxiing onto the runway.
The future of healthcare business intelligence will play out at the intersection of the pressing (and, for that matter, chronic) issues in business and policy, and the use of emerging analytical
capabilities to create applications to meet these challenges.
Dean Spitzer, in his wonderful book Transforming Performance Measurement, provides a survey of dozens of the most fruitful areas for investment in analysis and intelligence applications for any company in any industry. Adapted to the healthcare industry, this list includes applications for:
Some of these applications fall into the “hard,” quantifiable measurement category, but the real gains will be made in the softer areas that enable healthcare organizations to keep patients, providers, partners and, of course, customers.
The truly great uses of business intelligence in healthcare will be in an area where healthcare actually has an advantage over most other industries – discovery. The scientific discipline and penchant for finding patterns, testing alternatives and adapting new methods, processes and technologies (at least on the clinical side) is strong in the healthcare industry. Applied to the service, administrative and operational side of the enterprise, the evolving capabilities offered in a mature business intelligence environment have the potential to bring about profound and surprising innovations.
Recently, Forbes and MSN ran a slide show called “In Pictures: 15 People Who Changed The World.” Not surprisingly, eleven of the fifteen people changed our world through technology, and eight of those did so with medical technologies (structure of DNA, magnetic resonance imaging and the birth control pill).
But one of the achievements in the related Forbes article that I found particularly interesting was neither medical nor technological. It was Malcolm McLean’s 1956 invention of the shipping container. This discovery was the result of observation and is so mundane on the one hand (it’s just a box full of stuff) and so highly conceptual on the other hand (one must see the abstract equivalence of a box on a truck, a box on a ship and a box in a warehouse to get it) that it serves as a good analogy for the horizon in healthcare analytical applications. It is just this kind of “boring,” abstract pattern that business intelligence is meant to find.
Fortunately, this is just the kind of application where people trained in the health and medical disciplines really shine: that is, noticing patterns, gathering evidence, working through potential solutions in the field, etc., in order to improve the health of their patients, their communities and their practices.
Some examples of these types of applications may seem small. For instance, consider the RN who notices a pattern in the nursing notes between diabetic patients and difficulty getting rides to the clinic for appointments, and then successfully promotes a partnership between her organization and a local limo service. Better transportation, better health.
Still other examples are potentially ground-shaking patterns (small today, but with the potential to have a real impact in the future), such as:
Healthcare is an information-rich and analytically oriented field. We must use this fact to innovate at a faster rate on all sides of the business.
Get started now to understand the pressing trends and issues in healthcare, to understand the emerging capabilities of business intelligence, and to see the currently overlooked potential of analytic applications across the organization and the industry.
In this way, you will have a view for where healthcare business intelligence is heading and how your organization can stay ahead of the curve, improving life for patients, stakeholders and shareholders alike.
Thanks for reading!
Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington DC: National Academy Press, 2001.
Institute of Medicine. To Err is Human: Building a Safer Health System. Washington DC: National Academy Press, 2000.
Bodenheimer T, Grumbach K. Understanding Health Policy: A Clinical Approach. New York: McGraw-Hill, 2005.
Hanson C. W. Healthcare Informatics. New York: McGraw-Hill, 2006.
Jonas S. An Introduction to the U.S. Health Care System Third Edition. New York: Springer Publishing, 1992.
Shorter E. The Health Century. New York: Doubleday, 1987.
Eckerson W. Beyond the Basics: Accelerating BI Maturity. Renton WA: The Data Warehousing Institute, 2007.
Davenport T, Harris J. Competing on Analytics: The New Science of Winning. Boston: Harvard Business School Press, 2007.
Spitzer D. Transforming Performance Measurement: Rethinking the Way We Measure and Drive Organizational Success. New York: AMACOM, 2007.
Easteal S, Demosthenes P. View from the Future. IDG Communications, 2005. http://www.biotechnews.com.au/index.php/id;813620852;fp;;fpid;;pf;1
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