Dynamic health information system: Need of the hour to keep up with the momentum of ever-changing natural history of disease
Mitasha Singh, Shweta Goswami
Department of Community Medicine, ESIC Medical College and Hospital, Faridabad, Haryana, India
Shweta Goswami
ESIC Medical College and Hospital, Faridabad, Haryana
The ongoing pandemic of Covid 19 is different from previous ones in the sense that the data science combined with statistical analysis has countless applications in epidemiology of the disease. The final product of data analysis is usually of interest to the readers. However, through this article we bring forth the process of the data collection, movement and management at the ground level. The data generators and collectors are usually the health workers who were also the care givers during the pandemic. The duplication of record maintenance and data flow at multiple places by the burnt-out workers deteriorates the quality of data. Integrated database is the need of hour for quality health management system.
Singh M, Goswami S. Dynamic health information system: Need of the hour to keep up with the momentum of ever-changing natural history of disease.J Sci Soc 2021;48:112-115
Singh M, Goswami S. Dynamic health information system: Need of the hour to keep up with the momentum of ever-changing natural history of disease. J Sci Soc [serial online] 2021 [cited 2022 Jan 18 ];48:112-115
Available from: https://www.jscisociety.com/text.asp?2021/48/2/112/324076
The World Health Organization has declared the novel coronavirus, COVID-19, as pandemic. As of May 2021, India has the second-highest number of confirmed cases in the world (after the United States).To contain the spread of the virus, Haryana Government has strengthened the surveillance and control measures against the disease. As of May 14, cumulative 675,636 samples were found positive for COVID-19 in Haryana, cumulative positivity rate being 8.30%. If we go down to the district level, for example, one of the districts in Haryana, Faridabad, alone has a cumulative 93,853 positive cases (14% of samples found positive for COVID-19 in Haryana), 84,061 recovered/discharged cases, and 624 deaths till May 14, 2021.[1] We, epidemiologists, are variable lovers, but data generation to dissemination in times of COVID-19 was and is a real challenge. Challenge begins with mobilizing resources to generate relevant, essential data and sharing the same data with policymakers to be used in analysis, planning, implementation, and evaluation.
Data present a false sense of hope at times if the process from generation to dissemination gets contaminated. The plan for COVID-19 pandemic control is solely dependent on the natural history of the disease. Data play a vital role in documenting the natural history of any new disease. This pandemic has alarmed the country with a weak health management information system to strengthen it.
The data generation in case of COVID-19 starts from active and passive screening of individuals of symptomatic and asymptomatic. Once a person tests positive on any of the available tests, he/she is sent to either of the following: home isolation, designated COVID care centers (DCC), or dedicated COVID care hospitals (DCH) based on symptoms.[2] The course of illness is better appreciated in those admitted at DCC or DCH. This article focuses on the flow of data management (from data collection to information generation) and the problems incurred at a tertiary care center of North India.
The Ideal Data at District Level
To maintain paperless records, various computer-/mobile-based applications/portals have been created.
Screening: At the screening centers, the identifiers and particulars of the screened population are entered in this application. A unique identity number is generated for an individual who has been screened. This entry is further completed by the accredited laboratory to enter the COVID-positive or negative results using the same unique number[3]COVID-19 patient: Further, if the patient is COVID positive, his status of admission has to be updated in the same mobile-based application by the medical officer of the area where the patient resides. Another mobile-/computer-based application was created which is linked to the previous one to maintain a nationwide database for every patient admitted to a facility.[4] After the medical officer admits the patient in his area to a particular facility, he/she has to update the status of the patient and another unique surveillance identity number is created. The inpatient status of a patient is updated on a real-time basisAt the facility: Apart from patient-wise data storage applications, daily availability of beds, oxygen, and ventilators for a facility also needs to be updated from every center. In addition to the above computer-based applications, similar data but in a separate format need to be reported to the district, state, and central authorities twice daily. To be more comprehensive, it includes line listing of patients (with their identifiers such as age, sex, address, and contact number) attending influenza-like illness screening centers with their COVID status, new admissions, asymptomatic patients, discharges, current admissions with their daily status, and deaths in ward as well as at intensive care unit (ICU). Along with the line list, a cumulative number of above all variables are updated twice daily [Figure 1].{Figure 1}
Resources Needed in Generation of Above Data
The data generation needs workforce and logistics.[5] Workforce here stands for the doctors, nurses, and data entry operators/medical records technicians. Logistics are the working computer desktops, tablets, and active 24-h Internet connection. It requires training of the workforce regarding the type of data needed for a particular variable.
Actual data generation at the facility level
At outpatient department (OPD): The patient flow is a continuous process; hence, a time range is set for updating daily lists. The OPD attendees list is maintained in a register, a standard laboratory form, and the mobile application by the health-care worker. This is also maintained in spreadsheets daily. The nasopharyngeal/oropharyngeal samples are sent to laboratoryAt the laboratory: The data from the mobile application reaches the laboratory and further updated by doctors and staff working in the COVID laboratoryAt inpatient department: The new admissions, discharges, current admissions, asymptomatic patients, and death in ward and ICU in a facility are updated at midnight. All the COVID sheets filled by the treating doctor are uploaded on social media application groups (WhatsApp), as any type of fomite from COVID area cannot be transferred on the same day in non-COVID area. Likewise, the six-hourly census of ICU is updated on the same platform. The medical students and supervising doctors for data management update these on online spreadsheets. The line listing and cumulative numbers are updated in the morning and afternoon daily. The government web portals are updated daily as and when the information is updated in a spreadsheet by data entry operators. The deaths are reported within 6 h on chat groups of the ward/ICU with the computer typed death summaries in the desired format by the doctor on duty. This is further updated and conveyed by the team of data management doctors to the district and state authorities.
Barriers Faced
The aim of presenting an elaborate sequence of data collection and report formation is to bring forth the need of workforce and skills for the same. The COVID admission form at the entry of patient at DCH was many at times not filled or incomplete. Majority of times, there were wrong entries pertaining to patient identifiers or contact number of attendants. At times, the different identity numbers generated for a single patient had three different contact numbers or three different spellings of the name (Hindi names spelled in English). The same information being filled at multiple points is sometimes annoying to patients and their attendant that they turn giving wrong information [Figure 1]. Due to the unknown natural history of the disease, frequent new features/variables are asked for by the higher authorities from every center that too without a definition of each new variable. This created resistance from the paramedical staff and doctors. This was the product of the stress among the health-care workers due to postduty quarantine and stigma after one contracted the infection during patient care. Added to this, frequently changing demands of data created more resistance and interpersonal conflicts. Lack of supply of Internet connection or working computer system were other barriers felt by many. The health-care staff involved in maintaining the offline records were expected to send the same in an online mode as none of the papers/registers could be transferred from COVID to non-COVID areas. This led to a feeling of duplication of work.
Reasons for the Errors in Data Presentation
There is a communication gap between the officials who plan and devise the variables for which data have to be collected and the ground-level health-care staff who actually collect these data. The mere presence of workforce and logistics is not enough for data generation. It can be argued that in a situation of pandemic or emergency, it may not be feasible to train the staff. However, the online meeting platforms could be used for the same. The importance of type of data needed for any variable can be explained only before implementation of the data collection formats. Lack of training in computer especially online spreadsheets was a drawback among many. There are no training/standard operating procedures/operational definition/reference manual which can ensure the correctness of data being gathered. Another invisible component needed in generation of correct data is motivation and sincerity. Hence, there always remain missing data or mismatch of information available on one portal with the actual status. Data generated at the source are collected in conditions that result in mistakes and incomplete information by taking notes both on paper and electronically.
Good data management requires all data to reside at a central consolidated system to obtain a single view of each tested person and it would receive new data on a regular basis from all sources. This could have prevented burnout among health-care workers. The access of this single platform of COVID data must have been shared by all stakeholders (laboratory personnel, doctor, and policymakers) involved in data collection, analysis, and interpretation. The error related to the patient identifiers which created missing and duplicate patients from the database is the result of the above-mentioned complex system. This further impacts the information presented at the district, state, or national level. Data in COVID times were the only source of predictive models which helped in planning for saving lives.[6] Hence, an integrated database or platform is the need of the hour which improves the data quality and decreases the added stress on health-care workers [Figure 2].{Figure 2}
The authors extend their gratitude to each and every health-care worker who devoted his/her precious time in data generation during these tough times. We also thank the undergraduate students who helped in whatever way they can in the process of data collection and entry.
1 Media Bulletin on COVID-19, Health Department, Government of Haryana. Available from: haryanahealth.nic.in. [Last accessed on 2021 May 15].
2 Shalimar, Vaishnav M, Elhence A, Kumar R, Mohta S, Palle C, et al. Outcome of conservative therapy in coronavirus disease-2019 patients presenting with gastrointestinal bleeding. J Clin Exp Hepatol 2021;11:327-33.
3 Government of India. Indian Council of Medical Research. ICMR Covid 19 Data Portal. ISRM division of ICMR. Available from: https://cvstatus.icmr.gov.in/. [Last accessed on 2021 February 08].
4 Government of India. Ministry of Health and Family Welfare. COVID Facilities Portal. Available from: https://covid19.nhp.gov.in/facility/login. [Last accessed on 15 February 2021].
5 Henke N, Bughin J, Chui M, Manyika J, Saleh T, Wiseman B, et al. The age of analytics: Competing in a data-driven world. McKinsey Analytics 2016;pp 136. [Available from https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf] [Last accessed on 08 March 2021].
6 Data Management is Key to Containing Covid-19 and Future Pandemics. Attacama Blog. 2021. Available from: https://www.ataccama.com/blog/data-management-is-key-to-containing-covid-19-and-future-pandemics. [Last acessed on 2021 Feb 13].