DAVID C. KIBBE and BRIAN KLEPPER
Imagine that an innovative health plan – aware that half or more of health care cost is waste and that physician costs to obtain the identical outcome can vary by as much as eight fold – hopes to sweep market share by producing better quality health care for a dramatically lower cost. So it begins to evaluate its vast data stores. It’s goal is to identify the specialists, outpatient services and hospitals within each market that, for episodes of specific high-frequency or high value conditions, consistently produce the best outcomes at the lowest cost. Imagine that, because higher quality is typically produced at lower costs – there are generally fewer complications and lower incidences of revisiting treatment – the health plan will pay high performers more than low performers. Just as importantly, it will limit the network, steering more patients to high performers and away from low performers.
Suddenly, it will become very important for physicians and other providers to understand, in detail, how they compare to their peers within specialty, and how to provide the best care possible. And if they find the results aren’t so positive, they may want to figure out where their deficiencies lie, and how they can improve.
Now imagine that clinicians could easily view data about their patients and themselves.
- A problem list based on diagnoses within the past year.
- A list of medications prescribed, including ordering physician, dates and fulfillment information.
- A list of lab tests ordered, by physician and date.
- A list of immunizations.
Suppose the clinician could review, revise or copy this information to create a lasting “patient profile,” saving it online and retrieving it for use at each subsequent visit as appropriate.
Now imagine that this same Internet-based application provides a report based on aggregated patient claims data as current as 8-10 days old, and not just a single health plan’s patients, but from all payers. The kinds of reports or “dashboards” available would include, but not be limited to:
- A count of patients with particular diagnoses or conditions, by provider.
- A count of medications ordered, by most to least common.
- A count of lab tests ordered, by most to least common.
- Average number of visits per day, week, month.
- Percentages of patients meeting targets for key metrics (e.g., blood pressure control, diabetes screening testing, smoking cessation).
- Days before payment broken down by insurance companies and health plans.
Then add some basic clinical decision support:
- Analytics to identify patients at risk for chronic diseases or major acute events during the next year.
- Care gap analyses to create lists of actionable care items for each patient, based on the information in claims, drug, lab and electronic health records.
- Artificial-intelligence (AI) driven diagnostic aids.
- Best practice guidance.
- Online, real-time access to all prescriptions previously filled by the patient, along with automatic drug interaction information.
Now suppose that each physician or clinician could:
- Invite other physicians to provide their (de-identified) data to be pooled and compared with others in the pool.
- Select benchmarks from local, state, regional, and national data sets to compare each physician’s quality, safety and episodic cost performance.
- Start conversations and discussion groups with other physicians based upon questions raised by the data and its analytical indications: e.g., performance, the data itself, its reliability, evidence for higher or lower utilization, etc.
- Begin to assemble the components of a “meaningful use” EHR technology suite that will meet the requirements for EHR incentive payments starting in 2011.
We could pose lots of additional “what ifs,” but you get the picture. Information is available now from clinical records, claims, drug and lab orders, and could be provided to all clinicians in a manner that:
- When reported in the aggregate, is completely de-identified,
- Is compliant with applicable privacy and security laws and regulations, and
- Comes with an explicit invitation to make suggestions about how to improve the data’s quality, accuracy, currency and integrity.
We are very near to a ‘tipping point’ that will make physicians, medical practices, and provider organizations of all kinds very hungry for these kinds of data. There is growing pressure to control cost, both through reform and the marketplace, and moves afoot to significantly penalize physicians and organizations that have unnecessarily high costs.
In all businesses but health care, success is impossible without good information about customers and performance. By contrast, the combination of fee-for-service reimbursement and a lack of cost/quality transparency have let health care business achieve financial success, even when the business itself is oblivious to its performance. Physicians, hospitals and other health care organizations have made so much money, and have increased their incomes/revenues so easily by simply increasing demand, that rigorous monitoring of quality and financial status have been discouraged.
We believe that is all about to change. There is now evidence that health care purchasers (Medicare, health plans, employers, patients) have reached the limits of their capacity to pay and are “putting the money on the stump and running.” In most metropolitan markets, and to a lesser degree in rural markets, health care organizations’ efficiency and productivity will become increasingly critical to their survival.
As we run out of resources to spend on fee-for-service health care, the payment methodology will change to reflect selectivity in purchasing and contracting for services. Affordability and value will become more important than ever.
The government is leading the trend towards health care data collection and transparency of reporting. ARRA/HITECH and the Meaningful Use EHR incentive programs are notable for their emphasis on data collection for both performance and quality measurement.
When one of us (DCK) was in his early forties, he designed and taught courses on data and information management for Don Berwick’s Institute for Healthcare Improvement (IHI). Dr. Berwick was never a big proponent for computerization, but he was a stickler on data and its uses for understanding process and outcomes, and for guiding improvement efforts on a continuous basis. He believed strongly in comparing people, teams, and organizations in a collaborative fashion, and using gaps in performance as a stimulus to change for the better. Now he’s running the Center for Medicare and Medicaid Services, CMS. Chances are very good he’ll likely to continue to push for improved data.
We think it should be possible to create a multi-level offering of data, information, and EHR technology services to physicians and practices at a very reasonable cost. Every element in this article’s lists is not only imagine-able, but do-able already.