Innovations in health information technologies for chronic pulmonary diseases
Open accessAbstract
Asthma and chronic obstructive pulmonary disease (COPD) are common chronic obstructive lung disorders in the US that affect over 49 million people. There is no cure for asthma or COPD, but clinical guidelines exist for controlling symptoms that are successful in most patients that adhere to their treatment plan. Health information technologies (HITs) are revolutionizing healthcare by becoming mainstream tools to assist patients in self-monitoring and decision-making, and subsequently, driving a shift toward a care model increasingly centered on personal adoption and use of digital and web-based tools. While the number of chronic pulmonary disease HITs is rapidly increasing, most have not been validated as clinically effective tools for the management of disease. Online communities for asthma and COPD patients are becoming sources of empowerment and support, as well as facilitators of patient-centered research efforts. In addition to empowering patients and facilitating disease self-management, HITs offer promise to aid researchers in identifying chronic pulmonary disease endotypes and personalized treatments based on patient-specific profiles that integrate symptom occurrence and medication usage with environmental and genomic data.
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These individuals often have the poorest understanding of the disease, low use of maintenance medications, and poor health outcomes.
“These individuals often have the poorest understanding of the disease, low use of maintenance medications, and poor health outcomes.122”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [122]
The Background’s health-disparities section (page not provided) identifies health literacy and medication non-adherence as factors in chronic pulmonary disease disparities. Another Background passage links poor adherence to adverse clinical outcomes. The paper does not establish that the unspecified individuals have the poorest disease understanding or specifically low use of maintenance medications. (LLM, conf=0.79)
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- Authors
- Himes, B. E., & Weitzman, E. R.
- Affiliations
- University of Pennsylvania (US)
- Boston Children's Hospital (US)
- Corresponding author
- Himes, Blanca E.
- Venue
- Respiratory Research
- Published
- 2016-04-05
- Volume
- 17
- Issue
- 1
- Pages
- 38-38
- ISSN
- 1465-9921 (ISSN-L)
- 1465-993X
- PMC
- PMC4822326
- OpenAlex
- W2319414121
- Cited by
- 54
- References
- 64
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 3 · 2022: 5 · 2021: 6 · 2020: 13 · 2019: 9 · 2018: 6 · 2017: 8
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Respiratory Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R00 HL105663)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Health Sciences › Medicine › Physiology › Asthma and respiratory diseases
- Keywords
- health information technology, asthma, chronic obstructive pulmonary disease, chronic lung disease, disease self-management, patient empowerment, online patient communities, patient-oriented research, endotypes, personalized medicine, digital health tools, clinical validation
- MeSH terms
- Humans, Lung Diseases, Obstructive, Medical Informatics, Patient Education as Topic, Patient Participation, Self Care, Social Support, Telemedicine, Consumer Health Information, Social Networking, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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