Artificial intelligence and remote patient monitoring in US healthcare market: a literature review
ABSTRACTBackground: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solution...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Journal of Market Access & Health Policy |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20016689.2023.2205618 |
| _version_ | 1797226036254998528 |
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| author | Ayushmaan Dubey Anuj Tiwari |
| author_facet | Ayushmaan Dubey Anuj Tiwari |
| author_sort | Ayushmaan Dubey |
| collection | DOAJ |
| description | ABSTRACTBackground: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions.Objectives: Identify and summarize FDA approved RPM devices to provide information for the US medical device industry based on previous approvals and the markets’ needs.Methods: We searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions.Results: A total of 47 RPM devices were reviewed, among which 12.8% were classified as a De Novo product and the remaining devices fell under the 510(K) FDA category. The cardiovascular (74%) AI RPM solutions dominated the US market, followed by ECG-based arrhythmia detection algorithms (59.4%), and Hemodynamics and Vital Sign monitoring algorithms (21.9%). The trend observed in the FDA rejected devices was their inability to be classified into clinically relevant categories (Criteria 2 and 3).Conclusion: The market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality. |
| first_indexed | 2024-03-11T11:56:57Z |
| format | Article |
| id | doaj.art-8f3943e390f5482794395f06da3906c0 |
| institution | Directory Open Access Journal |
| issn | 2001-6689 |
| language | English |
| last_indexed | 2024-04-24T14:18:31Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Market Access & Health Policy |
| spelling | doaj.art-8f3943e390f5482794395f06da3906c02024-04-03T08:11:24ZengMDPI AGJournal of Market Access & Health Policy2001-66892023-12-0111110.1080/20016689.2023.2205618Artificial intelligence and remote patient monitoring in US healthcare market: a literature reviewAyushmaan Dubey0Anuj Tiwari1Independent Researcher, Rising JuniorMarket Access Advisor, Medspacetech, Tilburg, The NetherlandsABSTRACTBackground: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions.Objectives: Identify and summarize FDA approved RPM devices to provide information for the US medical device industry based on previous approvals and the markets’ needs.Methods: We searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions.Results: A total of 47 RPM devices were reviewed, among which 12.8% were classified as a De Novo product and the remaining devices fell under the 510(K) FDA category. The cardiovascular (74%) AI RPM solutions dominated the US market, followed by ECG-based arrhythmia detection algorithms (59.4%), and Hemodynamics and Vital Sign monitoring algorithms (21.9%). The trend observed in the FDA rejected devices was their inability to be classified into clinically relevant categories (Criteria 2 and 3).Conclusion: The market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality.https://www.tandfonline.com/doi/10.1080/20016689.2023.2205618Food and Drug Administrationremote patient monitoring devicesDe Novohospitalization ratesAI algorithms in medical technologyartificial intelligence in radiology |
| spellingShingle | Ayushmaan Dubey Anuj Tiwari Artificial intelligence and remote patient monitoring in US healthcare market: a literature review Journal of Market Access & Health Policy Food and Drug Administration remote patient monitoring devices De Novo hospitalization rates AI algorithms in medical technology artificial intelligence in radiology |
| title | Artificial intelligence and remote patient monitoring in US healthcare market: a literature review |
| title_full | Artificial intelligence and remote patient monitoring in US healthcare market: a literature review |
| title_fullStr | Artificial intelligence and remote patient monitoring in US healthcare market: a literature review |
| title_full_unstemmed | Artificial intelligence and remote patient monitoring in US healthcare market: a literature review |
| title_short | Artificial intelligence and remote patient monitoring in US healthcare market: a literature review |
| title_sort | artificial intelligence and remote patient monitoring in us healthcare market a literature review |
| topic | Food and Drug Administration remote patient monitoring devices De Novo hospitalization rates AI algorithms in medical technology artificial intelligence in radiology |
| url | https://www.tandfonline.com/doi/10.1080/20016689.2023.2205618 |
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