Abstract
Technology adoption can improve healthcare delivery, outcomes, and cost-efficiency. Using the COVID-19 pandemic as a foundation, this chapter discusses technology's role during the crisis and explores the barriers to successful healthcare management strategies due to inadequate or inappropriate technology adoption. This chapter also explores the pros and cons of technology and provides tools to help determine when adoption will benefit healthcare providers, systems, and consumers. The author discusses operational effectiveness and efficiency, effective communication, team coordination, health outcomes, data collection and utilization, and risk management in this chapter.
TopBackground
Health care leaders are charged with ensuring that public health and medical needs are met and afforded an appropriate level of focus, correct information, and response. The challenge during the COVID-19 pandemic was not only managing the financial burden of the disease, but also supporting and meeting the expectations of governments, health care providers, payers, and consumers alike. In the panic that surrounded the COVID-19 pandemic, health care systems were expected to give their immediate and undivided attention to an anxious and fearful public, in an environment of political posturing, misinformation, and a dearth of clinical guidelines. The directive was to track and trace the virus’ rapid spread across this globe, prevent exposure, and identify and treat those who became ill. Since face-to-face interactions were limited by time, social distancing, manpower limitations, and physical and emotional exhaustion, leaders acknowledged the urgent need to incorporate strategies afforded by technologies. In the early stages of the COVID-19 pandemic, a barrage of new cases overwhelmed hospitals and health care systems. Nevertheless, it was rapidly accepted that technology could help in the fight (Whitelaw, 2020) .
Over the last two decades, health care systems have battled severe acute respiratory syndrome (SARS), Zika virus, Ebola virus, and more recently, COVID-19 which, according to the WHO, had 542,817,862 confirmed cases and 6,337,288 deaths in 288 countries as of June 16, 2022 (World Health Organization, 2022). Undoubtedly, the best way to prepare for future pandemics is for health care leaders, clinicians, and policy makers to partner globally and invest in systems that can capture data and identify threats in real time (Buckee, 2020).
Health care leaders must be prepared for further crises and ensure strategies are in place to mitigate the devastation and loss that accompanies a regional or worldwide event as was seen with COVID-19. Leaders must also be cognizant of the socio-political and socio-economic factors that provide support or erect barriers to adopting new strategies within their system and stand ready to guide governments and other decision makers to effectively management expectations and promote optimal outcomes within their purview.
Key Terms in this Chapter
Health Level Seven International (HL7): A non-profit organization that is accredited by the American National Standards Institute, to develop and provide frameworks and standards for data sharing, integration, and the retrieval of clinical health data and other electronic health information.
Telehealth: The delivery of health care, health education, and health information remotely via telecommunications technology. This may include video conferencing, mobile health apps, remote patient monitoring, text messaging, and telephone communications.
Geographic Information Systems (GIS): A system that creates, manages, analyzes, and geographically displays referenced information. It uses data that is attached to a unique location.
Social media: Websites ad applications that enable the user to create and share content or to participate in social networking. Examples include Facebook, Twitter, Instagram, and TikTok.
Spatial Statistical Mapping: The application of statistical concepts and methods to data that have a spatial location attached to them, and in which the locational elements is used as an important and necessary part of the analysis.
Big Data: Extremely large data sets that may be analyzed to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
Electronic Medical (or Health) Record: A electronic (digital) collection of medical information about a person that is stored on a computer. It is an electronic version of the patient chart.
Blockchain Technology: A system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system.
Resource References: General references between resources.
Artificial Intelligence (AI): The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Fast Health Interoperability Resources (FHIR): An interoperability standard for the electronic exchange of healthcare information. FHIR was developed by Health Level Seven International (HL7) and quickly became the standard used by EMR entities and major health IT vendors. FHIR is designed specifically for web-based programs.
HL7 Interface: An interface used by healthcare organizations to enable messaging between clinical applications. For example, a lab can send test results electronically to the patient chart, or a physician can send a prescription to a pharmacy or a referral for radiology exams electronically.