Using Prediction to Improve Patient Flow in a Health Care Delivery Chain
Often, in a health care delivery chain, lack of coordination has been detrimental to timely, high quality care. This paper focuses on the two steps of the hospital health care delivery chain, an emergency department and a hospital’s inpatient units. Past research into this chain has suggested t...
| Main Authors: | , , , , |
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| Format: | Technical report |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/84017 |
| _version_ | 1826205468424404992 |
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| author | Peck, Jordan Gaehde, Stephan Benneyan, James Graves, Stephen Nightingale, Deborah |
| author_facet | Peck, Jordan Gaehde, Stephan Benneyan, James Graves, Stephen Nightingale, Deborah |
| author_sort | Peck, Jordan |
| collection | MIT |
| description | Often, in a health care delivery chain, lack of
coordination has been detrimental to timely, high quality
care. This paper focuses on the two steps of the hospital
health care delivery chain, an emergency department and a
hospital’s inpatient units. Past research into this chain has
suggested that early prediction of patient need for
admission can be used to better align flow between the two
departments. This chain and the nature of prediction in
health care delivery are discussed as well as a how
prediction may be useful in this context. Finally tools for
making admission predictions are tested and their possible
implications are explored. The results of this exploration
show that both expert opinion and a Naïve Bayesian
statistical approach have predictive value in this context. |
| first_indexed | 2024-09-23T13:13:20Z |
| format | Technical report |
| id | mit-1721.1/84017 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:13:20Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | mit-1721.1/840172019-04-11T03:27:59Z Using Prediction to Improve Patient Flow in a Health Care Delivery Chain Peck, Jordan Gaehde, Stephan Benneyan, James Graves, Stephen Nightingale, Deborah health care delivery chain prediction Often, in a health care delivery chain, lack of coordination has been detrimental to timely, high quality care. This paper focuses on the two steps of the hospital health care delivery chain, an emergency department and a hospital’s inpatient units. Past research into this chain has suggested that early prediction of patient need for admission can be used to better align flow between the two departments. This chain and the nature of prediction in health care delivery are discussed as well as a how prediction may be useful in this context. Finally tools for making admission predictions are tested and their possible implications are explored. The results of this exploration show that both expert opinion and a Naïve Bayesian statistical approach have predictive value in this context. 2014-01-15T19:08:07Z 2014-01-15T19:08:07Z 2011 Technical report http://hdl.handle.net/1721.1/84017 Attribution-NonCommercial-ShareAlike 3.0 United States http://creativecommons.org/licenses/by-nc-sa/3.0/us/ application/pdf |
| spellingShingle | health care delivery chain prediction Peck, Jordan Gaehde, Stephan Benneyan, James Graves, Stephen Nightingale, Deborah Using Prediction to Improve Patient Flow in a Health Care Delivery Chain |
| title | Using Prediction to Improve Patient Flow in a Health Care Delivery Chain |
| title_full | Using Prediction to Improve Patient Flow in a Health Care Delivery Chain |
| title_fullStr | Using Prediction to Improve Patient Flow in a Health Care Delivery Chain |
| title_full_unstemmed | Using Prediction to Improve Patient Flow in a Health Care Delivery Chain |
| title_short | Using Prediction to Improve Patient Flow in a Health Care Delivery Chain |
| title_sort | using prediction to improve patient flow in a health care delivery chain |
| topic | health care delivery chain prediction |
| url | http://hdl.handle.net/1721.1/84017 |
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