key: cord-255956-xfky1q4p authors: Narayan, Venkataraman; Hoong, Poon Beng; Chuin, Siau title: Innovative Use of Health Informatics to Augment Contact Tracing during the COVID19 Pandemic in an Acute Hospital date: 2020-08-24 journal: J Am Med Inform Assoc DOI: 10.1093/jamia/ocaa184 sha: doc_id: 255956 cord_uid: xfky1q4p This case report described the innovative design and build of an algorithm that integrated available data from separate hospital-based informatics systems that perform different daily functions to augment the contact tracing process of COVID-19 patients through identifying exposed neighboring patients and healthcare workers and assess their risk. Prior to the establishment of the algorithm, contact tracing teams comprising six members each would spend up to 10 hours to complete contact tracing for five new COVID-19 patients. With the augmentation by the algorithm, we observed ≥60% savings in overall manhours needed for contact tracing when there were five and above daily new cases through a time-motion study and Monte-Carlo simulation. This improvement to the hospital’s contact tracing process supported more expeditious and comprehensive downstream contact tracing activities as well as improved manpower utilization in contact tracing. Contact tracing is the process of identifying, assessing, and managing people who have been exposed to the virus to prevent onward transmission. [1] As a densely populated city state, timely identification and isolation of people exposed to the virus to reduce further spread through efficient and effective contact tracing is a key national strategy of Singapore to manage the COVID-19 pandemic. [2] Government agencies and healthcare institutions carry out contact tracing in an integrated manner for all COVID-19 patients. The contact tracing process in our hospital and how it interfaced with the Ministry of Health's (MOH) community-level contact tracing is illustrated in Figure 1 . The process was initiated at the hospital upon the diagnosis of a COVID-19 patient. As only 1% of cases have an incubation period of longer than 14 days, [3] the activity map, which comprised minute-to-minute details on the COVID-19 patient's activities for the period starting from 14 days before the onset of symptoms to the point the patient was admitted to the designated COVID-19 facilities in the hospital, was obtained through a phone interview. The activity map included specifying the time and areas the patient had been to, the people he/she had encountered and their contact information. In order to identify the healthcare workers (HCWs) and other patients who were in contact with the index case during this period, the clinical notes, patient movement charts, other patients' locations, and staff rosters were reviewed and cross referenced by the contact tracing team. Subsequently these preliminarily identified HCWs were interviewed via telephone to verify their contact with the COVID-19 patient, the nature of their exposure, and the personal protective equipment (PPE) they were attired in to assess their risk of contracting the virus. This was a time consuming and labor-intensive investigative process. Healthcare workers and other patients assessed to be at a higher risk of infections based on the prevailing infection control guidelines were isolated at home or in the wards and monitored for symptoms. The hospital's contact tracing process must be expeditious and comprehensive to support downstream activities in the community as well as the hospital itself. The information from the activity mapping and contact tracing of other patients and HCW was shared with MOH within 24 hours so that follow up actions of community-level contact tracing and isolation of the at-risk groups would be timely. This benchmark was in line with the US-CDC Interim guidance for Risk Assessment and Management of healthcare personnel with potential exposure to COVID-19 that stated that the care team contact tracing process should be completed within 24 hours of each case's identification. [4] Data scientists, operations managers and clinical staff worked closely to integrate data available in the informatics systems with human-based interviews to improve the timeliness, comprehensiveness and efficiency of the contact tracing process. A data-mining algorithm was developed to integrate the available data from hospital-based informatics systems that perform various day-to-day functions to augment the contact tracing process of COVID-19 patients to identify exposed HCWs and neighboring patients. The algorithm would run and generate a customized contact tracing report for each COVID-19 patient provided these information for the period the patient was in the hospital: the patient's presence in the various areas and the time period, patients at the same area during the same time period, and HCWs who attended to the patient at the various areas. The contact tracing team then scope the contacts to be interviewed based on this comprehensive report. HCWs who attend to more than one COVID-19 patient will be reflected as a contact in each of their reports. The algorithm and contact tracing reports were piloted and finetuned for the first 50 Covid-19 positive patients admitted to our hospital. It was evaluated for its accuracy and effectiveness in improving the contact tracing process. We observed significant time savings for our staff performing the detailed activity mapping and the report gave a reliable validation reference for the final contact racing reports. ( In the COVID-19 pandemic, expedient identification of individuals with significant exposure to COVID-19 patients is a key strategy to break the chain of transmission and flatten the epidemiology curve. A literature review of the databases of PUBMED, Cochrane, and Embase, with the search terms of "contact tracing" and "COVID-19" did not return any studies that described an integrated use of hospital informatics systems to augment contact tracing. With the increasing number of new cases diagnosed daily, the capacity for timely contact tracing would have to be met by increasing staff numbers to perform interviews of the COVID-19 patient and the contacts. The algorithm's value-add was the rapid and comprehensive identification of the COVID-19 patient's activity as well as individuals at risk -HCWs and other patients, to be interviewed. The contact tracing staff could then focus on the interviews and risk assessment of the contacts. As a result of better efficiency in manpower utilization, we are also able to maintain a lean contact tracing team despite the hospital experiencing an increasing number of COVID-19 patients. The algorithm was limited in real-time data content because of its dependency on the eHints data repository, which is designed as a midnight snapshot server with daily data uploads scheduled at 2359 hours. Hence for patients who presented at A&E and admitted on the same day, the contact tracing reports were supplemented with screenshots from the live EMR system. The time needed for this manual sequence was negligible and can be automated in future. Close interaction between the data scientist, operations managers and clinical staff were essential in the design, improvement, and operationalization of the above contact tracing model that integrated the use of data from informatics system with interview-based contact tracing by the contact tracing teams. This model delivered faster contact tracing in the hospital, hence supporting more expeditious and comprehensive downstream contact tracing activities, as well as improved manpower utilisation in contact tracing. This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Implementation and management of contact tracing for Ebola virus disease Evaluation of the Effectiveness of Surveillance and Containment Measures for the First 100 Patients with COVID-19 in Singapore -US CDC Morbidity and Mortality Weekly Report The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application Guidance for Risk Assessment and Public Health Management of Healthcare Personnel with Potential Exposure in a Healthcare Setting to Patients with 2019 Novel Coronavirus (2019-nCoV) The authors have no competing interests to declare.