Financial-services providers outspend the health-care industry on information technology, but they haven't made good use of all that data
When it comes to information technology spending, I've often been told companies in the health-care industry should behave more like banks.
During the decade I've been a chief information officer, IT operating budgets have been 2% of my organization's total budget. That proportion is typical for health care. During the same period, IT budgets for the financial-services industry have averaged 10% or higher.
Given the recent troubles of AIG (AIG), Lehman Brothers, Merrill Lynch, Washington Mutual, and others, you have to wonder whether those IT budgets represent money well spent.
Of course, financial-services firms have had great systems for handling such tasks as share trading, disaster recovery, and data storage. But did they have the business-intelligence tools and dashboards that could have alerted decision makers about the looming collapse of the industry?
Too Much Data
Did the financial-services industry have controls, risk analysis, or a memory of previous crises ranging from the Depression to the Japanese banking debacle to the collapse of Enron and WorldCom? Was it greed, irrational expectations, or too much data and not enough wisdom that brought down these institutions?
We in the health-care profession naturally take no delight in the financial industry's descent. At the same time, we're trying to make the most of our IT spending and make wise choices with the data we've amassed.
One of the challenges of being a doctor in the 21st century is information overload. More medical literature is published every year than a doctor can read in a lifetime. As electronic health records become more common, doctors can be overwhelmed with data gathered about each patient. They do not want to review hundreds of normal findings; they want to know the information that can be acted on to keep patients healthy.
Health-care CIOs need to implement applications that filter data so that it becomes information, that transform information into knowledge, and that ultimately provide clinicians with wisdom based on that knowledge exactly when they need it.
Suppose a patient's blood pressure is 100/50, typically considered low but not necessarily problematic. That's data. Now suppose the patient has a 10-year history of relatively high blood pressure of 150/100. That's more information. Finally, suppose the patient has a known history of high cholesterol and is now experiencing chest pain. The sudden drop in blood pressure could indicate a heart attack in progress. That's knowledge. The patient needs an aspirin, oxygen, and nitroglycerine medication immediately. That's wisdom.
Recently I asked my primary-care physician to export my entire history from his electronic medical record system. Although I'm a completely healthy person, the result was a 77-page document. It contains a mix of administrative and clinical data, numeric observations, and unstructured text. It would take a physician about an hour to navigate through it.
How can we turn such data into information? Over the past few years, my clinical information-systems team has built what's known as "event-driven medicine" into our applications. These tools help us translate data from events such as changes in medication, patient visits for diagnostic testing, lab results, or newly discovered allergic reactions into actionable wisdom.
Here are three examples:
When a doctor writes a prescription for medication, a query is sent to our regional e-prescribing system to determine the patient's insurance coverage for pharmaceuticals. Based on the answer, we access the appropriate payer-specific list of covered drugs so that all medications are chosen to minimize cost and maximize effectiveness for each patient.
Every prescribed drug also is checked against the entire history of the patient's active medications from pharmacy and payer databases throughout the country. Safety issues, guidelines, and best practices are displayed to the clinician, ensuring quality care. When the correct, safe medication in the right dose is selected, it is instantly routed to the pharmacy of the patient's choice, going from the doctor's brain to the patient's vein without any handwriting or human interpretation. All of this happens in real time, based on the data found in electronic health records, information about trends in body functions, knowledge from decision-support databases, and wisdom—via interconnected Web services—from the orchestration of all these moving parts behind the scenes. All of that ultimately provides the best choice for each medication.
When a doctor orders a radiology test, a query is sent to a decision-support engine. More than 1,000 best-practice rules from the American College of Radiology and the world's radiology literature are examined, along with patient medications, laboratories, allergies, and demographics, to select the most appropriate test. Radiology exams are scored from 5 stars to 1 star, balancing efficacy, risk, and cost. If a clinician orders one of these tests, a pre-authorization is sent to the payer in real time, and the test is automatically approved. All of this happens in a few seconds, using patient data plus the knowledge from the literature to yield a wise choice for diagnostic testing.
When a doctor identifies a chronic disease condition, a decision-support "screening sheet" is created to track all the events in a patient's care. Diabetic tracking includes lab results, eye exams, foot exams, immunizations, and weight. The sheet is updated with each event, such as a lab result or appointment. The tool makes recommendations for care, such as "patient is past due for an eye exam" or "patient should receive pneumovax [a vaccine against infection] this season." Clinicians do not need to focus on the raw data. Instead, they can review suggestions in real time to optimize the care of the patient.
One thing is certain: In 2009, no one is going to tell me that health-care IT should run as well as that of Lehman Brothers. I've even talked to folks in the IT industry who are rewriting their Web sites and résumés to remove historical references to their overwhelming successes in financial-services automation.
I have empathy for everyone in the financial-services industry; the anxiety and stress must feel overwhelming. Given that every person in the U.S. will be paying thousands of dollars in additional taxes over time to rescue the industry, we're all going to accept responsibility for its IT systems and for the management that led the unsinkable ship of the banks into an iceberg at full speed.
Let us hope that the next generation of financial-industry business intelligence tools and dashboards provides the wisdom to help us avoid such disasters in the future.