The previous chapter described the next generation of personal health records as “patient-centric” and introduced the concept of observations of daily living. The term “observations of daily living” was coined by Project HealthDesign, a $10 million national program of the Pioneer Portfolio at the Robert Wood Johnson Foundation. Observations of daily living (ODLs) are the patterns and realities of daily life that have never before considered to be part of one’s health record, such as diet, physical activity, quality and quantity of sleep, pain episodes and mood. ODLs are an area of PHR development that is genuinely user-directed; not only are the data patient-centric by definition, but so too are the health-related activities that stem from those data. This chapter explores observations of daily living and the potentially groundbreaking insights they hold for patients and clinicians alike.
Observations of daily living are inherently patient-centered because they speak to the patient’s everyday experience. Previously, doctors collected information in the clinical setting—indicators like vital signs, blood pressure, and other measures like height and weight that are useful to the clinician. But people do not live in the doctor’s office, and what is important to providers is not always the most interesting or useful information for patients. Questions such as whether variations in stress, exercise, and diet affect the frequency and intensity of pain have much more relevance to someone who suffers from chronic pain than traditional health-related information. PHRs and ODLs enable patients to collect and interpret data from their daily lives in a new and highly personalized way.
Observations of daily living paint a more personalized picture of each individual patient, making it possible for patients to compare themselves to…themselves. Consider an example from Project HealthDesign E-Primer #3, “Health in Everyday Living.” According to Jay Sanders, M.D., president and CEO of Global Telemedicine Group, “the American Heart Association defines “normal” blood pressure as lower than 120/80—based on millions of patients, regardless of age, gender or other factors that are bound to influence one’s blood pressure....But consider a woman whose normal blood pressure is 90/60. If her BP rises to 100/70 at her next checkup, and six months later it’s 110/75, for her, that’s hypertension—and it would never get picked up in the course of a normal clinical encounter, because we’re usually looking for deviations from a norm that has nothing to do with her.” In essence, ODLs create a dynamic, personalized, living database for tracking and analyzing an individual’s health.
Moreover, ODL data serve as the foundation for the specifically tailored recommendations that sophisticated PHRs can provide. An advanced PHR application might collect a patient’s pain episodes, sleep patterns, and exercise habits and be able to make personalized recommendations for minimizing chronic back pain. For example, the PHR could analyze the number or intensity of pain episodes during a given week and report that the patient reports more intense pain on days that she gets less than eight hours of sleep. Going further, the PHR might present data showing the patient that, nine times out of 10, more intense pain prevented her from exercising. Depicting these data in a graph, the PHR would be able to demonstrate the consequences of health behaviors vividly and in a personalized fashion. The Project HealthDesign teams found that patients are highly receptive to learning lessons based on their own data—especially when the information is presented in context, as opposed to three to six months later in a doctor’s office. Patient-sourced data add immense value for people with chronic illnesses, who can use the information to make decisions about their health that will enable them to feel and function at their best on a daily basis. Patients can gather and analyze their own ODL data via PHRs to determine ways to lead healthier lives, rather than simply managing their illness.
The Project HealthDesign teams conducted focus groups with patients which suggested that, even though ODLs are collected outside of the doctor’s office, they would nonetheless serve to enrich the clinical encounter. Patients said that not only would ODLs facilitate their engagement with health-related decisions, but ODLs would also enable them to have more productive conversations with their physicians. Observations of daily living give health care providers a window into the patient’s daily health that doctors seldom glimpse, and lead to insights that are unattainable if records only contain data that are captured in the clinical setting. In the future, ODLs could be analyzed within the context of a patient’s genetic data; or, they could be used to strengthen the public health system or inform practices for evidence-based medicine. As Steve Downs, S.M., Assistant Vice President of the Health Group at the Foundation, points out in a post on Pioneering Ideas, the information typically stored in a medical record is just “The Tip of the Iceberg.”
Observations of daily living present patients with the opportunity to better understand and manage their health, but they also create unique challenges and questions. For example, who determines what constitutes the official medical record (and, by extension, which data receive strict privacy protection)? How should we balance the need to minimize the patient’s burden for data collection with the desire for reliable and useful data? Downs considers the question of the medical record in a post on Pioneering Ideas; and Lygeia Ricciardi, lead blogger for Project HealthDesign, outlines some of the challenges that patients face in her post “Collecting Observations of Daily Living.”
With each individual representing a rich and virtually unlimited source of health-related information, one of the most interesting questions to emerge from Project HealthDesign is how to choose which ODLs should be collected for which patients. Opening the data flood gates is not necessarily the best strategy to improve patients’ understanding of health-related choices. Large amounts of raw data are likely to overwhelm the individual and clinician alike; and thus the challenge is to develop creative, effective ways to capture, store and glean meaning from observations of daily living. What patients need are smart, interpretive and intuitive tools that turn data into useable information in order to identify important health information amidst the noise.