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Designing and validating a low-cost real time locating system to continuously assess patient wait times
[Display omitted] •RFID sensors were used in an ophthalmology clinic for passive real time localization.•Sensors had a sensitivity to identify location to a spatial position of
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| Published in: | Journal of biomedical informatics 2020-06, Vol.106, p.103428-103428, Article 103428 |
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| container_title | Journal of biomedical informatics |
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| creator | Newman-Casey, Paula Anne Musser, John Niziol, Leslie M. Shedden, Kerby Burke, David Cohn, Amy |
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•RFID sensors were used in an ophthalmology clinic for passive real time localization.•Sensors had a sensitivity to identify location to a spatial position of |
| doi_str_mv | 10.1016/j.jbi.2020.103428 |
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•RFID sensors were used in an ophthalmology clinic for passive real time localization.•Sensors had a sensitivity to identify location to a spatial position of <1 m.•Sensors had a sensitivity to identify spatial position at a frequency of <1 min.•Customizing off-the-shelf passive RFID sensors enabled system cost to be 10% of vendor price.•Hidden Markov Modeling of the raw data gave ≥80% accuracy compared to direct observation.
Outpatient clinics lack infrastructure to easily measure and understand patient wait times. Our objective was to design a low-cost, portable passive real time locating system within an outpatient clinic setting to measure patient wait times and patient-provider interactions.
Direct observation was used to determine workflow in an outpatient glaucoma clinic at the University of Michigan. We used off-the shelf, antenna-integrated ultra-high frequency (UHF) RFID readers (ThingMagic, Astra-Ex, Woburn, MA) and UHF re-useable passive RFID tags (Zebra Impinj Monza 4QT, Seattle, WA). We designed a custom RFID management application in the Java programming language that was equipped with ‘live’ device administration to collect time and location data from patients and providers. These hardware choices enabled low cost system installation. Hidden Markov Modeling (HMM) was used to smooth patient and provider location data. Location data were validated against direct observations and EHR evaluation.
The HMM smoothed RFID system data accurately predicted patient location 80.6% of the time and provider location 79.1% of the time, compared to direct observation locations, an improvement over the raw RFID location data (65.0% and 77.9% accurate, respectively). Patient process time was on average 42.8 min (SD = 27.5) and wait time was 47.9 min (SD = 33.1). The installation and recurring capital costs of the system are approximately 10% of available commercially-supplied patient/provider tracking systems.
Passive RFID time study systems can enable real-time localization of people in clinic, facilitating continuous capture of patient wait times and patient-provider interactions. The system must be tailored to the clinic to accurately reflect patient and provider movement.
Capturing wait time data continuously and passively can empower continuous clinical quality improvement initiatives to enhance the patient experience.</description><identifier>ISSN: 1532-0464</identifier><identifier>ISSN: 1532-0480</identifier><identifier>EISSN: 1532-0480</identifier><identifier>DOI: 10.1016/j.jbi.2020.103428</identifier><identifier>PMID: 32339749</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Computer Systems ; Humans ; Low-cost design ; Outpatient clinical operations ; radio frequency identification (RFID) ; Radio Frequency Identification Device ; real-time locating systems (RTLS) ; Waiting Lists ; Workflow</subject><ispartof>Journal of biomedical informatics, 2020-06, Vol.106, p.103428-103428, Article 103428</ispartof><rights>2020 Elsevier Inc.</rights><rights>Copyright © 2020 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-60eb7c7d41cd2baa0adf7eb3cb250c6b19e4c6d9049eeede9b57a7381c5683003</citedby><cites>FETCH-LOGICAL-c451t-60eb7c7d41cd2baa0adf7eb3cb250c6b19e4c6d9049eeede9b57a7381c5683003</cites><orcidid>0000-0002-0847-1929</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32339749$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Newman-Casey, Paula Anne</creatorcontrib><creatorcontrib>Musser, John</creatorcontrib><creatorcontrib>Niziol, Leslie M.</creatorcontrib><creatorcontrib>Shedden, Kerby</creatorcontrib><creatorcontrib>Burke, David</creatorcontrib><creatorcontrib>Cohn, Amy</creatorcontrib><title>Designing and validating a low-cost real time locating system to continuously assess patient wait times</title><title>Journal of biomedical informatics</title><addtitle>J Biomed Inform</addtitle><description>[Display omitted]
•RFID sensors were used in an ophthalmology clinic for passive real time localization.•Sensors had a sensitivity to identify location to a spatial position of <1 m.•Sensors had a sensitivity to identify spatial position at a frequency of <1 min.•Customizing off-the-shelf passive RFID sensors enabled system cost to be 10% of vendor price.•Hidden Markov Modeling of the raw data gave ≥80% accuracy compared to direct observation.
Outpatient clinics lack infrastructure to easily measure and understand patient wait times. Our objective was to design a low-cost, portable passive real time locating system within an outpatient clinic setting to measure patient wait times and patient-provider interactions.
Direct observation was used to determine workflow in an outpatient glaucoma clinic at the University of Michigan. We used off-the shelf, antenna-integrated ultra-high frequency (UHF) RFID readers (ThingMagic, Astra-Ex, Woburn, MA) and UHF re-useable passive RFID tags (Zebra Impinj Monza 4QT, Seattle, WA). We designed a custom RFID management application in the Java programming language that was equipped with ‘live’ device administration to collect time and location data from patients and providers. These hardware choices enabled low cost system installation. Hidden Markov Modeling (HMM) was used to smooth patient and provider location data. Location data were validated against direct observations and EHR evaluation.
The HMM smoothed RFID system data accurately predicted patient location 80.6% of the time and provider location 79.1% of the time, compared to direct observation locations, an improvement over the raw RFID location data (65.0% and 77.9% accurate, respectively). Patient process time was on average 42.8 min (SD = 27.5) and wait time was 47.9 min (SD = 33.1). The installation and recurring capital costs of the system are approximately 10% of available commercially-supplied patient/provider tracking systems.
Passive RFID time study systems can enable real-time localization of people in clinic, facilitating continuous capture of patient wait times and patient-provider interactions. The system must be tailored to the clinic to accurately reflect patient and provider movement.
Capturing wait time data continuously and passively can empower continuous clinical quality improvement initiatives to enhance the patient experience.</description><subject>Computer Systems</subject><subject>Humans</subject><subject>Low-cost design</subject><subject>Outpatient clinical operations</subject><subject>radio frequency identification (RFID)</subject><subject>Radio Frequency Identification Device</subject><subject>real-time locating systems (RTLS)</subject><subject>Waiting Lists</subject><subject>Workflow</subject><issn>1532-0464</issn><issn>1532-0480</issn><issn>1532-0480</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UctuEzEUHSEQfcAHsEFespngtzNCQkIFClKlbsra8tg3wdHMOPg6qfL3OJ0S0U1X93XO8fU9TfOO0QWjTH_cLDZ9XHDKj7WQfPmiOWdK8JbKJX15yrU8ay4QN5QyppR-3ZwJLkRnZHferL8CxvUUpzVxUyB7N8TgykNJhnTf-oSFZHADKXGE2vLzFA9YYCQlEZ-m2tmlHQ4H4hABkWwrCKZC7l0sD0R807xauQHh7WO8bH59_3Z39aO9ub3-efXlpvVSsdJqCr3xJkjmA--doy6sDPTC91xRr3vWgfQ6dFR2ABCg65VxRiyZV3opKBWXzedZd7vrRwi-bpHdYLc5ji4fbHLRPp1M8bddp701gktKTRX48CiQ058dYLFjRA_D4Caof7RcdEpzbbiqUDZDfU6IGVanZxi1R4PsxlaD7NEgOxtUOe__3-_E-OdIBXyaAVCvtI-QLfp6Sw8hZvDFhhSfkf8LY7mkTA</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Newman-Casey, Paula Anne</creator><creator>Musser, John</creator><creator>Niziol, Leslie M.</creator><creator>Shedden, Kerby</creator><creator>Burke, David</creator><creator>Cohn, Amy</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0847-1929</orcidid></search><sort><creationdate>20200601</creationdate><title>Designing and validating a low-cost real time locating system to continuously assess patient wait times</title><author>Newman-Casey, Paula Anne ; Musser, John ; Niziol, Leslie M. ; Shedden, Kerby ; Burke, David ; Cohn, Amy</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-60eb7c7d41cd2baa0adf7eb3cb250c6b19e4c6d9049eeede9b57a7381c5683003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Systems</topic><topic>Humans</topic><topic>Low-cost design</topic><topic>Outpatient clinical operations</topic><topic>radio frequency identification (RFID)</topic><topic>Radio Frequency Identification Device</topic><topic>real-time locating systems (RTLS)</topic><topic>Waiting Lists</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Newman-Casey, Paula Anne</creatorcontrib><creatorcontrib>Musser, John</creatorcontrib><creatorcontrib>Niziol, Leslie M.</creatorcontrib><creatorcontrib>Shedden, Kerby</creatorcontrib><creatorcontrib>Burke, David</creatorcontrib><creatorcontrib>Cohn, Amy</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of biomedical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Newman-Casey, Paula Anne</au><au>Musser, John</au><au>Niziol, Leslie M.</au><au>Shedden, Kerby</au><au>Burke, David</au><au>Cohn, Amy</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Designing and validating a low-cost real time locating system to continuously assess patient wait times</atitle><jtitle>Journal of biomedical informatics</jtitle><addtitle>J Biomed Inform</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>106</volume><spage>103428</spage><epage>103428</epage><pages>103428-103428</pages><artnum>103428</artnum><issn>1532-0464</issn><issn>1532-0480</issn><eissn>1532-0480</eissn><abstract>[Display omitted]
•RFID sensors were used in an ophthalmology clinic for passive real time localization.•Sensors had a sensitivity to identify location to a spatial position of <1 m.•Sensors had a sensitivity to identify spatial position at a frequency of <1 min.•Customizing off-the-shelf passive RFID sensors enabled system cost to be 10% of vendor price.•Hidden Markov Modeling of the raw data gave ≥80% accuracy compared to direct observation.
Outpatient clinics lack infrastructure to easily measure and understand patient wait times. Our objective was to design a low-cost, portable passive real time locating system within an outpatient clinic setting to measure patient wait times and patient-provider interactions.
Direct observation was used to determine workflow in an outpatient glaucoma clinic at the University of Michigan. We used off-the shelf, antenna-integrated ultra-high frequency (UHF) RFID readers (ThingMagic, Astra-Ex, Woburn, MA) and UHF re-useable passive RFID tags (Zebra Impinj Monza 4QT, Seattle, WA). We designed a custom RFID management application in the Java programming language that was equipped with ‘live’ device administration to collect time and location data from patients and providers. These hardware choices enabled low cost system installation. Hidden Markov Modeling (HMM) was used to smooth patient and provider location data. Location data were validated against direct observations and EHR evaluation.
The HMM smoothed RFID system data accurately predicted patient location 80.6% of the time and provider location 79.1% of the time, compared to direct observation locations, an improvement over the raw RFID location data (65.0% and 77.9% accurate, respectively). Patient process time was on average 42.8 min (SD = 27.5) and wait time was 47.9 min (SD = 33.1). The installation and recurring capital costs of the system are approximately 10% of available commercially-supplied patient/provider tracking systems.
Passive RFID time study systems can enable real-time localization of people in clinic, facilitating continuous capture of patient wait times and patient-provider interactions. The system must be tailored to the clinic to accurately reflect patient and provider movement.
Capturing wait time data continuously and passively can empower continuous clinical quality improvement initiatives to enhance the patient experience.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32339749</pmid><doi>10.1016/j.jbi.2020.103428</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-0847-1929</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Systems Humans Low-cost design Outpatient clinical operations radio frequency identification (RFID) Radio Frequency Identification Device real-time locating systems (RTLS) Waiting Lists Workflow |
| title | Designing and validating a low-cost real time locating system to continuously assess patient wait times |
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