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Technology and the Changing World of Case Investigation and Contact Tracing

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Ramona Nelson, PhD, BC-RN

Citation: Nelson, R., (May 18, 2021) "Technology and the Changing World of Case Investigation and Contact Tracing" OJIN: The Online Journal of Issues in Nursing Vol. 26, No. 2.

DOI: 10.3912/OJIN.Vol26No02InfoCol01

The healthcare disaster created by COVID-19 increasingly requires technology-based solutions to address the need for effective and efficient case investigation and contact tracing. This column explores the use of technology-based approaches for finding patients with COVID-19, case investigation, and following up with their contacts, or contact tracing. Using this information as a foundation, I also suggest in this column how these same technologies might provide innovative approaches to identifying patients before they are aware of many illnesses or even at a point that might be called pre-illness. For the purposes of this column, pre-illness is defined as a point where changes have started to occur in the body but there are no indications of illness.

Developing technology-based approaches to finding patients with COVID-19 and following up with their contacts has been driven by an interest in improving the efficiency and effectiveness of the manual processes for case investigation and contact tracing. Case investigation and contact tracing are two fundamental public health activities that involve working with a patient who has been diagnosed with a probable or actual infectious disease to identify contacts who may have been infected through exposure to the patient. These activities have been used by public health professionals for decades to control the spread of tuberculosis, sexually transmitted infections, HIV, and other infectious diseases. Today these same well-honed skills are being used with COVID-19. (National Center for Immunization and Respiratory Diseases [NCIRD], Division of Viral Diseases, 2021)

Case Investigation involves interviewing clients with COVID-19, eliciting their close contacts, monitoring the clients for COVID-19 symptoms, and connecting clients to resources to support self-isolation. Contact Tracing involves notifying contacts of their exposure, referring them to testing, monitoring them for COVID-19 symptoms, and connecting contacts to resources to support self-quarantine (NCIRD, 2021).

Using Technology to Support Case Identification

The process of case investigation begins with case identification or case finding, meaning finding patients with the disease. Currently, this process begins when a patient tests positive for COVID-19. However, there are a number of factors related to the transmission of COVID-19, and testing for COVID-19, that make the process of starting case investigation when a patient tests positive ineffective. Some of the more challenging factors include:

  1. The time between the onset of the infection when patients are contagious, and the presentation of symptoms, is usually between 2 and 14 days. During this time, patients may have no idea they are incubating and spreading COVID-19.
  2. The severity of the illness experienced by patients varies greatly from very mild, cold-like symptoms to life threating illness. Patients with mild to moderate symptoms may not seek or even be encouraged to seek medical treatment.
  3. Some patients who are infectious never have symptoms. The studies estimating the percent of patients who are asymptomatic vary widely (Johansson, et al., 2021; Pollock & Lancaster, 2020; Yanes-Lane, et al., 2020) This wide variation may occur first because different populations vary in the rate of asymptomatic cases. For example, the rate of asymptomatic cases may be higher in children. Second, reported research studies have used a wide variation of follow-up techniques. For example, patients who are followed-up three weeks after initial testing may demonstrate a higher rate of symptom development than patients who are followed-up one week after initial testing.
  4. Patients who have no symptoms, are pre-symptomatic, or have symptoms with different levels of intensity may vary greatly in their ability to transmit COVID-19 to contacts.
  5. With limited testing, resources for testing have focused on patients who have symptoms, especially those who are more acutely ill.
  6. Without a physician’s prescription, testing can be expensive.

As a result of these factors, waiting until testing has established the presence of the COVID-19 is much too late to effectively slow the spread of the disease. But how can a patient be diagnosed before they even know they are ill? From a public health perspective, how can case finding be initiated with patients who demonstrate limited or no symptoms and have never been tested for the disease?

One approach to case finding is to test a large part of the population before they are symptomatic or even in the pre-illness stage. Many current screening tests for cancer or heart disease use this methodology. Screening mammograms for all women over a certain age are done to find a much smaller group of women who are developing cancer but have no signs or symptoms. Weighing children on an annual or semiannual basis is done to prevent childhood obesity, and in turn, adult obesity, as well as health problems linked to obesity. But screening a large part of the population can be labor intensive and expensive. This is especially true if medical equipment and personnel are required.

With the use of technical approaches, the initial work of screening can begin with the patient, thereby decreasing labor costs and making the process more effective. When screening begins with a patient-initiated case investigation, the process begins much earlier in the disease course. In addition, access to screening becomes more available at any time of the day or night. There are several technology-based tools that can assist with case finding. These are discussed in more detail below. But first, it is important to note that these technical tools do not diagnose disease. These tools collect data from patients and use algorithms to analyze data and identify patients in need of additional work-up. Questions one might ask in evaluating these screening tools include:

  • Who has access to the tool? For example, many online screening tools developed by universities and other institutions can only be accessed by employees and students.
  • Are patients able to use the tool easily and correctly? Is the tool sensitive to various levels of digital, information, and/or health literacy?
  • How is privacy managed? What personal information must be provided to use the tool? Who has access to that data?
  • Who is providing this tool and why are they providing it? For example, companies (e.g., healthcare institutions or lab companies) that manufacture these tools, or use the data generated, can use these tools to generate business. A government site may be using these tools to track location of outbreaks and provide public health services.
  • How reliable is the tool and how was this determined? A tool may pick up almost every case of COVID-19 (a highly sensitive tool) however, the tool may not eliminate other illnesses with related symptoms (a low specific tool). For example, fever is a symptom of COVID-19, however it is not specific to COVID-19. Therefore, screening for fever alone is not an effective approach to screen for COVID-19.
  • What is the quality of the feedback provided to patients? Can patients act on the information provided in their local area?

For the purpose of discussion within this column, these tools are grouped into three categories. This section will discuss online survey or screening tools for reporting symptoms; wearables for monitoring physiological data; and tools for tracking exposure to pathogens or toxins.

Online Survey or Screening Tools
One of the first approaches to patient-initiated COVID-19 screening was online screening tools. Online screening tools have been in existence for several decades. With COVID-19, patients answer a series of questions concerning COVID-19 related symptoms that they are or are not experiencing. The online tools then provide feedback to patients about next steps. Links to various examples of these types of tools can be seen in Table 1. Readers are also encouraged to search the internet and try several other such tools.

Table 1. Examples of Online Patient-Initiated Technology-Based Screening Tools

Provider

Link

CDC

https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/coronavirus-self-checker.html

Tanner Health System

https://www.tanner.org/patients-and-visitors/patient-services/coronavirus/covid-19-screening-questionnaire

Indiana University Health

https://iuhealth.org/covid19/virtual-coronavirus-screening

Google

https://landing.google.com/screener/covid19?source=google

While patient-initiated, technology-based screening tools are very useful to direct patients to seek needed medical care, they have one big disadvantage. These tools require patients to realize that they have a problem – an illness. But COVID-19, like many other healthcare problems, begins before patients know that they have a problem. More expensive and difficult to implement wearable devices, referred to as “wearables,” may identify an illness before the patient is aware of the problem or before the problem is fully developed. For example, a wearable device may identify patients at risk for pre-diabetes.

Consumer Wearable Devices
Wearables are a category of electronic devices that can be worn as accessories, embedded in clothing, implanted in the user's body, or even tattooed on the skin. These devices are powered by microprocessors and include the ability to send and receive health or medical data via the Internet (Hayes, 2020). Initially, the types of data collected by wearables was somewhat limited (e.g., pulse rate). However, as the range has expanded to include such data elements as an electrocardiogram or oxygen saturation, the significance of these devices in healthcare has expanded.

As more people started to use these devices, and the range of data collected by them expanded, research studies that considered the potential of providing patients with an early alert system became of interest. One of the largest collections of completed and ongoing studies related to this topic is located at Stanford University (Stanford University, n.d.a). One study that might be of special interest to healthcare providers is (as of this publication) recruiting people who hold high-risk jobs, such as healthcare workers, grocery workers, teachers, and students on campus, or living with someone who is at high risk of exposure to Covid-19 (Stanford University, n.d.b).

The process of using wearable devices as an approach to case investigation is still very much in the research stage. Research questions focused on the health-related implications of these devices usually center around three questions:

  1. What tools and methods can be used to collect patient data from these devices?
  2. What is the best way to analyze and interpret the data that is collected?
  3. How can we use a patient centered approach to integrate the data, information, and knowledge generated by these tools into healthcare for patients, families, groups, and communities?

An additional key question for nurses and other healthcare providers is: What are the privacy and ethical issues in collecting, analyzing, and using data from these resources?

At this point most research is focused on the first and second questions. For example, one research study in 2017 used over 250,000 daily measurements, such as skin temperature and pulse, from 43 individuals and concluded that wearable devices were useful to identify early signs of Lyme disease. They based their conclusion on the observation that selected measurements increased above the individual’s baseline days before clinical symptoms of Lyme were noted. (Li, Salins, Zhou, Zhou, & chuÈssler-Fiorenza Rose, 2017). Another study collected physiological and activity data from 5262 participants. Over the time of the study 32 individuals in the cohort became infected with COVID-19. Retrospective analysis of these data demonstrated that 63% of the COVID-19 cases could have been detected before symptom onset. (Mishra, Wang, & Metwally, 2020)

Research studies concerning early alerts or pre-illness detection of disease has not been limited to infectious diseases. An interesting example is reported in a publication titled “Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology” (Baig, Mirza, GholamHosseini, Gutierrez, & Ullah, 2018). A list of research studies of this type can be seen on a website maintained by Hexoskin at https://www.hexoskin.com/pages/scientific-publications (Carre Technologies Inc (Hexoskin), n.d.).

Digital Contact Tracing Apps
Digital contact tracing uses an app installed on an individual’s smartphone to determine contact between that individual and infected patients who are using the same app and have reported their infection. Once contact has been established, the individual is alerted of exposure using the contact app. What is actually traced is not the contact between individuals but the contact between the phones, based on the assumption that the phones are being carried by the individuals.

Digital contact tracing apps have been developed using one of two different technical approaches. The first technical approach tracks the location of the phone using GPS with triangulation from nearby cell towers to find other phones that are in the same location. Constantly tracking a person’s location can be considered a significant invasion of privacy and is unacceptable to many people. The second approach uses Bluetooth. With this approach, phones in close proximity swap encrypted tokens with any other nearby phones. Bluetooth technology makes it easier to anonymize the date and provides better privacy protection than location tracking (Cyphers & Gebhart, 2020).

Within the United States most current contact-tracing apps are being built by individual state or territorial governments. With these apps fully operational in 19 states, Washington, DC, Guam, and Puerto Rico, as of December 2020, almost half of all Americans live somewhere covered by an app. Additional states are currently developing or piloting digital contact tracing apps. A list of these apps, along with evaluation data for each app, can be accessed online at https://bit.ly/38OWuCN (Sato, 2020). At face value, this approach appears to have a great deal of promise. However, there have been several challenges and acceptance has been much less than expected. Several of the reasons for non-acceptance of this approach are listed in Table 2

Table 2. Reasons for Low Acceptance and Effectiveness of Digital Contact Tracing Apps

To function effectively these apps must be accepted and used by a significant percentage of the population. However, for many reasons, adoption remains low with almost all states experiencing single digit acceptance (de la Garza, 2020).

Many people, especially people of color, often distrust the authorities with good reason, and consider handing information over to the government for contact tracing a nonstarter (Muscato, 2021).

While Bluetooth technology provides needed privacy protection, the lack of location data made it impossible for public health professionals to determine where clusters were forming or how the disease was spreading (Anushka, 2020).

Many people in the population at highest risk do not own a smartphone; many people who do own a smartphone lack the digital literacy knowledge and skills to effectively install and use these types of apps.

The lack of a coordinated national effort in the United States has created a patchwork of apps that launched at different times and often did not function across local borders (de la Garza, 2020).

Many apps were launched after widespread community transmission had already occurred (de la Garza, 2020).


Conclusion

Delivery of healthcare, and the roles of healthcare providers, are being radically changed by technology. In this column I have discussed the impact of technology on contact tracing and case investigation. This impact is demonstrated in three stages of change (see Table 3) that occur when technology is incorporated into healthcare-related processes.

Table 3. How Technology Changes Healthcare

Stage

Degree of Change

1

Makes the established procedure or process more efficient and effective.

2

Changes how the process or procedure is completed.

3

Creates a new process or procedure.

In stage one, technology improves the efficiency and effectiveness of an established process. For example, the use of a tablet with a preformatted form and appropriate prompts makes it possible for nurses to complete a much more comprehensive assessment in less time than the previous manual process. The assessment process has not really changed – it has just become more effective and efficient. In the context of COVID-19, having staff, students, and faculty at a university complete an online screening tool each morning makes the process of case identification much more efficient and effective.

The second stage changes the process itself and opens new options. For example, with elective admissions, patients can go online preadmission and complete part of the assessment process. This makes it possible for the patient and/or caregiver to review previous information and provide more comprehensive data than might be remembered if the nurse collects the data on admission. If this more comprehensive data indicates that additional testing is warranted, it can be completed before admission. Discharge planning can be started preadmission. Equipment needed for post discharge care can be in place at home and tested even before admission. An example of this might be checking in advance the fit of a shower chair in one’s home, and the room to get in and out with crutches.

Historically, contract tracing required patients to remember details about where they may have been exposed to a disease and who they might have exposed to that disease. Digital contact tracing apps by tracing contact between smartphones uses a completely different process for finding exposure and alerting patients of their exposure.

The third stage changes the process itself, as in the following two examples. Technology is currently changing the teaching-learning process. Historically, most teachers used verbal communication in the form of a lecture supplemented with selected reading to inform learners of course content. In the flipped classroom, teachers develop learning experiences which require students to actively engage with the content. With the increased use of consumer wearable devices, the concept of using early diagnosis to improve patient outcomes may become outdated. Instead, we have begun moving to a healthcare system where early warnings and interventions can be implemented pre-illness.

It is important to note that moving from stage 1 to stage 3 should not be conceived as walking up a set of steps. Rather this occurs more like walking up a slippery incline. Sometimes one slips back and must get a better grip (or a better technology) before moving forward. In living and working in healthcare, we cannot cling to the process, the procedure, or the technology of today. Rather our focus must be on the goal – the mission of better health for individuals, families, groups, and communities – as we move forward on the slippery incline of implementing innovative technological approaches to support healthcare delivery.

Author

Ramona Nelson, PhD, BC-RN
Email: ramonanelson@verizon.net

Dr. Nelson holds a baccalaureate degree in nursing from Duquesne University, and master’s degrees in both nursing and information science, as well as a PhD in education, from the University of Pittsburgh. Prior to her current position as President of her own consulting company, she was Professor and Chair of the Department of Nursing at Slippery Rock University in Pennsylvania. In the early 1980’s, one of Dr. Nelson’s employee benefits, while teaching at the University of Pittsburgh, was the opportunity to take university courses for only $5.00 a credit. After taking courses in computer assisted instruction and theory for information science, she recognized these tools (computers) just might be somewhat useful at the bedside and in the classroom. She has been exploring and discovering just how useful they can be ever since.

Dr. Nelson’s more recent research and publications focus on the specialty area of health informatics. Health Informatics: An Interprofessional Approach (2014), co-authored with Nancy Staggers, received the first place American Journal of Nursing Book of the Year award for Information Technology/Informatics. The second edition (2018) continues today as a primary textbook in the field of health informatics. Based on her contributions to nursing and health informatics she has been inducted into both the American Academy of Nursing and the first group of fellows in the National League for Nursing Academy of Nursing Education. She has also been recognized as a pioneer within the discipline by the American Medical Informatics Association. Her goal in serving as Editor of OJIN’s Informatics Column is to open a discussion about how we as nurses in the inter-professional world of healthcare can maximize the advantages and manage the challenges that computerization brings to our practice.

References

Anushka. (2020, April 29). Disadvantages of bluetooth based tracking systems. Data Protection. Retrieved from: https://metacept.com/disadvantages-of-bluetooth-based-tracking-systems/

Baig, M., Mirza, F., Gholam Hosseini, H., Gutierrez, J., & Ullah, E. (2018). Clinical decision support for early detection of prediabetes and type 2 diabetes mellitus using wearable technology. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4456-4459. doi:10.1109/EMBC.2018.8513343.

Carre Technologies Inc (Hexoskin). (n.d.). Scientific Publications with Hexoskin. Retrieved from https://www.hexoskin.com/pages/scientific-publications 

Cyphers, B., & Gebhart, G. (2020, April 28). Apple and Google’s COVID-19 exposure notification API: Questions and answers. Electronic Frontier Foundation (EFF). Retrieved from: https://www.eff.org/deeplinks/2020/04/apple-and-googles-covid-19-exposure-notification-api-questions-and-answers

de la Garza, A. (2020, November 20). Contact tracing apps were big tech's best idea for fighting COVID-19. Why haven't they helped? Time. Retrieved from: https://time.com/5905772/covid-19-contact-tracing-apps/

Hayes, A. (2020, May 11). Wearable technology. Stocks. Retrieved from: https://www.investopedia.com/terms/w/wearable-technology.asp

Johansson, M. A., Quandelacy, T., Kada, S., Prasad, P. V., Steele, M., Books, J. T., Slayton, R., Biggerstaff, M., & Butler, J. C. (2021). SARS-CoV-2 transmission from people without COVID-19 symptoms. Journal of the American Medical Association (JAMA) Network Open, 4(1), e2035057. doi:10.1001/jamanetworkopen.2020.35057

Li, X. D., Salins, D., Zhou, G., Zhou, W., & chuÈssler-Fiorenza Rose, S. (2017, January 17). Digital health: Tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biololgy, 15(1): e2001402. doi:10.1371/journal.pbio.2001402

Mishra, T., Wang, M., & Metwally, A. (2020). Pre-symptomatic detection of COVID-19 from smartwatch data. National Biomedical Engineering, 4, 1208–1220. doi: 10.1038/s41551-020-00640-6

Muscato, L. (2021, February 24). Digital contract tracing. MIT Technology Review, pp. 45-46. Retrieved from: https://www.technologyreview.com/2021/02/24/1017803/digital-contact-tracing-app-covid/

National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases. (2021). Interim guidance on developing a COVID-19 case investigation & contact tracing plan: Overview. COVID-19, Health Departments. Retrieved: https://www.cdc.gov/coronavirus/2019-ncov/php/contact-tracing/contact-tracing-plan/overview.html

Pollock, M., & Lancaster, J. (2020). Asymptomatic transmission of covid-19. British Medical Journal: The BMJ, 1-2. doi: 10.1136/bmj.m4851

Sato, M. (2020, December 14). Contact tracing apps now cover nearly half of America. It’s not too late to use one. MIT Technology Review. Retrieved from: https://www.technologyreview.com/2020/12/14/1014426/covid-california-contact-tracing-app-america-states/

Stanford University, School of Medicine. (n.d.a). Precision health for everyone. Stanford Healthcare Innovation Lab. Retrieved from: https://innovations.stanford.edu/

Stanford University, School of Medicine. (n.d.b). Empatica: COVID-19 wearables study. Stanford Medicine Snyder Lab Studies, Department of Genetics. Retrieved from: https://snyderlabs.stanford.edu/empatica/

Yanes-Lane, M., Winters, N., Fregonese, F., Bastos, M., Perlman-Arrow, S., Campbell, J. R., & Menzies, D. (2020). Proportion of asymptomatic infection among COVID-19 positive persons and their transmission potential: A systematic review and meta-analysis. Plos One, 15(11), e0241536. doi:org/10.1371/journal.pone.0241536


© 2021 OJIN: The Online Journal of Issues in Nursing
Article published May 18, 2021

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