The Future of Data-Driven Wound Care
ABSTRACT Care for patients with chronic wounds can be complex, and the chances of poor outcomes are high if wound care is not optimized through evidence-based protocols. Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-spec...
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| Veröffentlicht in: | AORN journal 2018-04, Vol.107 (4), p.455-463 |
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| creator | Woods, Jon S., MD Saxena, Mayur, MS Nagamine, Tasha, MS Howell, Raelina S., MD Criscitelli, Theresa, EdD, RN, CNOR Gorenstein, Scott, MD, FACEP Gillette, Brian M., PhD |
| description | ABSTRACT Care for patients with chronic wounds can be complex, and the chances of poor outcomes are high if wound care is not optimized through evidence-based protocols. Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. This article provides an overview of the application of big data analytics and machine learning in health care, highlights important recent advances, and discusses how these technologies may revolutionize advanced wound care. |
| doi_str_mv | 10.1002/aorn.12102 |
| format | Article |
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Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. 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Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. This article provides an overview of the application of big data analytics and machine learning in health care, highlights important recent advances, and discusses how these technologies may revolutionize advanced wound care.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Big Data</subject><subject>Blood pressure</subject><subject>Clinical decision making</subject><subject>Clinical trials</subject><subject>Data analysis</subject><subject>dataset</subject><subject>Datasets</subject><subject>Decision making</subject><subject>Electronic health records</subject><subject>Health care</subject><subject>Hospitals</subject><subject>machine learning</subject><subject>Medical equipment</subject><subject>Medical records</subject><subject>Medical/Surgical</subject><subject>neural networks</subject><subject>Nursing</subject><subject>Patients</subject><subject>Physicians</subject><subject>Quality of care</subject><subject>Regulatory approval</subject><subject>Velocity</subject><subject>wound care</subject><subject>Wound healing</subject><issn>1878-0369</issn><issn>0001-2092</issn><issn>1878-0369</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kVFLwzAQx4Mobk5f_ABS0AcROnNJuqYvwticCsOBTnwMaXvFzq6dyTrZtzd1m4gP5iDJw-_-HL8j5BRoFyhl17oyZRcYULZH2iBD6VPei_Z__VvkyNoZdYez4JC0WBS4oqxNLqZv6I3qZW3QqzJvqJfaH5p8haX3WtVl6g20wWNykOnC4sn27ZCX0e10cO-PJ3cPg_7YTwQw5sdBqBOpNWAgEOJMxBHj7k5ClsaRlD2NnAueJWkqaBZKHTMItJQ0lXEKseAdcrnJXZjqo0a7VPPcJlgUusSqtopRiCQNRRA59PwPOqtqU7rpHNXjEgIhpaOuNlRiKmsNZmph8rk2awVUNe5U4059u3Pw2TayjueY_qA7WQ6ADfCZF7j-J0r1J0-Pu9CbTQ86b6scjUqKvMwTXbzjGu3P0KAsU1Q9NytrNgaSUwAJ_AsjvIs3</recordid><startdate>201804</startdate><enddate>201804</enddate><creator>Woods, Jon S., MD</creator><creator>Saxena, Mayur, 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| identifier | ISSN: 1878-0369 |
| ispartof | AORN journal, 2018-04, Vol.107 (4), p.455-463 |
| issn | 1878-0369 0001-2092 1878-0369 |
| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Artificial intelligence Big Data Blood pressure Clinical decision making Clinical trials Data analysis dataset Datasets Decision making Electronic health records Health care Hospitals machine learning Medical equipment Medical records Medical/Surgical neural networks Nursing Patients Physicians Quality of care Regulatory approval Velocity wound care Wound healing |
| title | The Future of Data-Driven Wound Care |
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