Observations of daily living
Observations of daily living (ODLs) are cues that people attend to in the course of their everyday life, that inform them about their health.[1][2][3]
ODLs are different from signs, symptoms, and clinical indicators in that they are defined by the patient, and are not necessarily directly mapped to biomedical models of disease and illness. Examples of ODLs include observations about sleep patterns, exercise behavior, nutritional intake,[4] attitudes and moods, alertness at work or in class, and environmental features such as clutter in the living or working space. Not all patient-generated data constitute ODLs. For example, a patient with diabetes may record their blood glucose levels every day at home, generating data to share with their clinician. That kind of patient-generated data is crucial to inform clinical decision making, but does not constitute ODLs. ODLs are typically defined by patients[5] and their families[6] because they are meaningful to them,[7] and help them self-manage their health[8] and make decisions about it.[9] ODLs may very well complement biomedical indicators and inform medical decision making by providing a more complete and holistic view of the patient as a whole person,[10] provided they are properly integrated in clinical workflows and supported by health information technologies.[11]
See also
References
- ^ Health in Everyday Living
- ^ Observations of Daily Living
- ^ Impact of Consumer Health Informatics Applications, Agency for Healthcare Research and Quality (AHRQ)
- ^ Foundations Striving to Prevent Obesity, Health Affairs
- ^ The Engaged E-patient Population
- ^ Tracking 'Observations of Daily Living' in Infants and the Elderly (WSJ)
- ^ The Data Driven Life (NYT)
- ^ How Life's Details Help Patients (WSJ)
- ^ The Decision Tree: Taking Control of Your Health in the New Era of Personalized Medicine
- ^ Jaen CR, Brennan, PF. MDs, Patients Need to Share Data; Technology Can Help Rheumatology News 2009 Dec;8(12):15-15.
- ^ Incorporating Patient Generated Data in Meaningful Use of Health IT, Testimony to the US ONC HIT Policy Committee