Richard B. Berlin, Jr., MD

Health Alliance Medical Plans, Urbana, Illinois

Bruce R. Schatz, PhD

CANIS Laboratory, University of Illinois at Urbana-Champaign

"Outcomes management is a technology of patient experience designed to help patients, payers, and providers make rational medical care-related choices based on better insight into the effect of these choices on the patient’s life. Outcomes management consists of a common patient-understood language of health outcomes; a national data base containing information and analysis on clinical, financial, and health outcomes that estimates as best we can the relation between medical interventions and health outcomes, as well as the relations between health outcomes and money; and an opportunity for each decision-maker to have access to the analyses that are relevant to the choices they must make.

Outcomes management would draw on four already rapidly maturing techniques. First, it would place greater reliance on standards and guidelines that physicians can use in selecting appropriate interventions. Second, it would routinely and systematically measure the functioning and well-being of patients, along with disease-specific clinical outcomes, at appropriate time intervals. Third, it would pool clinical and outcome data on a massive scale. Fourth, it would analyze and disseminate results from the segment of the database most appropriate to the concerns of each decision maker. This should also allow the entire outcomes management system to be modified continuously and improved with advances in medical science, changes in people’s expectations, and alteration in the availability of resources." -- Paul Ellwood, Shattuck Lecture, 1988 (1,p1551)

Much has been written of the effect of the Baby Boomer generation on our societal and economic systems. However, its effect on the American healthcare system has yet to be felt. This historically large population is now 50, and will soon be 65 years old. If there is concern in contemporary medicine about appropriate treatments and outcomes today, just wait until tomorrow. How can we cope in a constrained financial market, when we face the largest number of elderly ever? The impact on the provision of care, in a contracting medical marketplace and a constrained economic environment, will be profound.

The infrastructure to provide care at this scale does not exist (2,3). In 2011, the first of 77 million baby boomers reach retirement age; the age when health care costs start to escalate (4). In 2000, Medicare will have an estimated 40 million enrollees; in 2040, Medicare will have an estimated 81 million. It is clear that the economy will not be able to support all treatments for all the people all the time. We will have to squarely face the issue of appropriate outcomes for chronic diseases with chronic treatments.

The common thread connecting the problems of healthcare in the 21st century is a model of outcomes. Measuring outcomes gives the medical definition of "true health" for an individual or a population (5). It includes health status functionality and quality of life assessment. Yet there is little agreement as to how outcomes are defined (6). It was easier in medicine when treatment plans were focused on surgical management of acute illness. Then, measurements of survival and incidence of post-operative complications determined quality of care and patient outcomes.

Chronic illness is now the dominant feature of health care (7). Some 22% of primary care patients report a major complaint of chronic pain, and this impact will grow with the aging of the population (8). Arthritis is a chronic illness typical of the aging baby boomer population. Some 12% of the US population, some 30 million people, have arthritis (9). The clear determinants for acute illness are less applicable for chronic illness with its plethora of physical, psychological, social, economic, and personal factors (10).

Like other chronic illness, arthritis has exacerbations and remissions – the intensity and location of the pain change over time (11). It is known that the disease and symptoms wax and wane within the day (12), that disabilities one year may disappear the next, that one joint involvement may become several, or alter in significance over time (13).

There are excellent questionnaires for measuring outcomes for arthritis. However, their static administration lacks the regular interaction needed to provide a true outcome measure of treatment response and ability to function with the current state of the disease (14). A continual tracking of a patient's functional status would monitor and measure ability to use joints and remain active. Continual tracking would enhance utility of existing arthritis questionnaires (15).

A new model is needed to help measure and monitor a patient’s condition and response to therapy, particularly for chronic conditions. The proposed model provides a method to give the physician the detailed data necessary to determine the patient’s perception and functionality with regard to chronic illness. Using personal computers to access the Internet, an infrastructure is now feasible to continuously record an individual’s progress with disease.

"The Rand investigators made each sub-dimension of health, such as physical functioning, emotional well-being, general well-being, and social functioning, into measurable characteristics of individuals and populations through the development of specific assessment instruments. They showed how patients themselves could be sources of valid and reliable information on their own functional status, and they explored relationships between these patient-centered measurements and more classical medically-oriented measurements of physiological status and function. …

Rand's SF-36 is a tool for measurement of general health status that is applicable across a wide variety of health care conditions and types of encounter. Meanwhile, other researchers have developed more disease-specific assessment instruments targeted at symptoms, outcomes, and experiences associated with particular diagnoses. Important advances in disease-specific measurement have occurred, with applicability to such [chronic] conditions as arthritis, coronary heart disease, depression, and neurological impairment. Altogether, today's health services research has transformed the simple and elegant measurements of survival proposed by Codman in the early twentieth century into a robust and elegant ‘tool kit’ for the measurement of quality in its many and subtle dimensions." -- Donald Berwick in New Rules, 1996 (16, p115-116).

Tracking health status requires measurement instruments (17), such as questionnaires that the patient completes during diagnosis or treatment. General-health questionnaires, such as SF-36 (18,19), attempt to measure outcomes in an organized and standard fashion. There are nearly 1000 quality of life questionnaires, including such disease-specific ones as AIMS2 (20,21) for arthritis, which provide similar concrete assessment at a more detailed level. However, the effectiveness of these questionnaire instruments has been limited by their required short length and their administration over short periods of time (22). While a helpful addition to the physician’s means of collecting information, health status questionnaires have been static instruments, reflecting one moment in time.

These questionnaire instruments need to become more detailed and more dynamic, in order to become more widely used and useful. Patient derived health outcome data is rarely used in routine management of the patient (23). The number of questions in a standardized questionnaire is geared towards the attention span of a patient and the practical time constraints of the healthcare facility. This situation limits the length of the questionnaire to a few pages, yielding perhaps 30-40 questions. In addition, questionnaires have become so numerous and complex that they might be best administered by experts who understand their range of effectiveness (24).

Most patients’ perceptions of their personal health can be described by 10 broad categories (25,26). These categories encompass the major physical health factors, including: disease, health care, health function, genetic endowment, physical environment, social environment, individual response, behavior, biology, well-being, prosperity. Similarly, there are only a few important broad categories that encompass the major mental health factors. A standard list of 10 factors (27) includes: level of consciousness, emotional state, orientation, attention, memory, language, calculations, praxis, visual-spatial function, reasoning/abstractions.

The standard questionnaires today concentrate on disease diagnosis and physical manifestations, largely because they are compelled to be limited in focus. For example, arthritis questionnaires ask about ability to move joints and limitations placed on daily activity. However, they omit genetic background of the patient, which may be more important to assessing health status. If both of the patient’s parents were debilitated by arthritis at age 65, then the patient likely will be, no matter what his/her joint status at age 50.

How many questions would be necessary to completely and accurately capture the health status of a patient? There are roughly 1000 common diseases described in a diagnosis manual for general practitioners (28). If each disease has a questionnaire with 30 questions, then complete coverage would require 30,000 questions. Each of the 10 physical and mental factors requires perhaps 10 subcategories to elicit adequate information about the status. For example, the visual-spatial factor has subcategories (27) of: "orientation impaired, can’t drive, can’t use tools, route-finding problems, dressing difficulty, can’t copy figures, agnosia, apraxia".

Coverage for the health factors would thus require 100 physical and 100 mental subcategories. Accounting for the health factors for every disease would require 200*30,000 or 6 million questions. Although some subcategories would require fewer questions, additional questions would be needed for interactions across categories. Thus, a full health status questionnaire covering all category factors would certainly encompass several million questions in total. Given the necessity of asking the questions of each patient over time to record the dynamic changes in their conditions, the physician needs an instrument more like SF-36M (where M is Million) than the current SF-36.

The need to ask questions from a sample of millions will cause a radical paradigm shift in health assessment. To eliminate variation inherent in the current intermittent patient-physician interaction in obtaining health status, data must be standardized and accurately entered by patients and physicians alike. To enable patients at home to provide correct answers about health status within their attention span, questions must be asked on a situational basis, personalized to the present condition and the past answers of the particular patient.

Achieving this scale is simply not possible within the constraints of traditional office visits and paper questionnaires. Asking situational questions is quite feasible, however, via home computers over the Internet. Since the patients are describing their health on a continual basis at their convenience, the descriptions will be more detailed and accurate than current infrequent rushed interactions with healthcare providers.

An information infrastructure is needed to focus clinicians’ attention on changes in patients’ status, especially older persons with chronic conditions (7). Rather than returning to a physician's office only when a medical regime fails, a patient could interact electronically with a personal database that would track progress, including response to therapy and problems with disease. Frequent reassuring interaction would achieve a truer picture of the episodic nature of chronic disease and lead to a treatment pattern personalized to the particular individual.

The proposed outcomes model is based upon continual monitoring of the physical and mental health factors. These health monitors are similar in function to a heart monitor, which enables an individual patient to track personal cardiological functions and seek treatment when the functions fall outside safe parameters (29).

In the more general health case, the patient must interact with a tracking system to record personal health parameters on a continual basis. The dynamic descriptions of health parameters can then be used to provide individualized treatments. During the course of a chronic illness, for example, communications between monitors and healthcare providers could result in treatments rapidly varied to match the illness’ episodic nature.

The Internet provides the technology, for the first time, to continuously record an individual’s health, via direct interaction with each patient (30). Surveys indicate that many elderly persons already use the Internet from their homes and that this usage will increase as the baby boomers age (31). An Internet Health Monitor would be an interactive program used by each individual to generate a daily record of personal health parameters.

By daily interaction with dynamic versions of traditional questionnaires, monitoring can be done of an individual patient’s varying ability over time to cope with a disease. For example, an adaptation for daily interaction of existing questionnaires (32,33) might include: "Do you have stiffness in your knees? Is it greater or lesser than yesterday?" "Could you do your errands in the neighborhood today?" "Do you have stiffness in your hands? Is it greater or lesser than yesterday?" "Could you do your housework without help today?"

From the point of view of the healthcare delivery system, the interactive data model will produce an evolving picture of a patient at any given time, taking into account diagnoses and treatments, successes and setbacks. The system would assess the current level of functionality and interactively coach the patient to higher levels of functionality. The consistency of administration via computer would reduce the inadequacy of current questionnaires.

Periodically, this data would be reviewed (determined by medical parameters and health plan factors) by trained professionals adept at such data analysis. Such a record of a patient's and a disease's condition would identify changes that might be of importance and early intervention by a health care professional be recommended.

Collecting continual interaction with detailed patient status over the Internet will eventually develop a comprehensive healthcare database. Such a database would allow a similarity match between a patient’s clinical situation and a cohort of similarly described patients. This identification would give a realistic expectation of disease progression and treatment outcome, as measured over the population at large (34).

The rise of the Internet has made it economically feasible to support national-scale health monitors from home computers. Personal computers are now widely enough deployed throughout the general public that millions of Americans routinely browse information on the Web. According to national surveys, healthcare is the single most referenced topic, with some two-thirds of users having accessed such information (35). A Harris Poll in February 1999 estimated that 60 million Americans accessed health information on the Web in 1998, with 10% of the respondents searching for arthritis.

The document protocols in the World-Wide Web have standardized the data formats traditionally handled by diverse management information systems (36). PC-based Web-forms to database servers are rapidly substituting for MIS database entry to mainframes in many applications, including patient record systems in clinics and hospitals (37,38).

Patients are eager to learn about potential treatments and share experiences with similar patients. For example, experience with CHESS (39) showed that patients with severe diseases, such as AIDS, are quite willing to spend an hour a day on the computer, to read targeted brochures and chat with similar patients. Elderly women with breast cancer are willing to carry out daily interactions over the Net (40).

Initial users of Internet Health Monitors will likely be elderly patients, who already monitor their health continually by informal means. The most similar current situation is a triage nurse, who consults with patients over the telephone, using a heuristic computer program covering the most common complaints (41). Adding a health monitor continually available in the home setting, may help reassure patients and aid their progress.

Once many patients interact with health monitors, a patient record database will be generated at a detailed and daily level. Locating similar cases within this database will require handling the variability of terminology used by the different patients. A major technological obstacle to federation of electronic patient record systems has been the lack of concept search (42). Concept search can retrieve different records discussing the same topic (concepts) but using different terminology (words).

Information infrastructure is evolving to support concept search across heterogeneous sources. In less than ten years, the Internet will have transformed into the Interspace (43), where users navigate connections across concept spaces of information rather than transmit data across packet networks of computers. This will enable the federation of all the different information sources generated by different healthcare stakeholders into a single uniform source (44).

For example, a single search for "limited knee mobility due to osteoarthritis in elderly women" should retrieve relevant items from interactions across the country entered by different patients with different terminologies and analyzed by different physicians for different treatments. Concept navigation across the Interspace will help reduce patient variability for answers to health status questions, much as standardized questionnaires across the Internet will help reduce physician variability in asking the questions initially.

The next generation of personal computers will be powerful enough to compute concept spaces for all the knowledge of a community-scale collection. Healthcare providers in each clinic in each community can collect their local patient interactions and perform their own analysis to identify local patterns. These local patterns can be assembled into global patterns, using concept navigation across community collections. The resulting national patient database can then be searched to find similar cohorts for any individual case.