At the beginning of 2020, as the COVID-19 pandemic swept across the US in multiple waves, health systems had to rapidly develop systems for tracking various aspects related to managing the pandemic. This included not just overall trends in incidence, hospitalizations, and outcomes; but also metrics related to the response.
COVID-19 was the first pandemic in the United States since the widespread adoption of electronic health records incentivized by the Meaningful Use program. As a result, the availability of health information was much broader than in any previous pandemic. The widespread impact of COVID-19 also meant that every healthcare institution was affected, and was tracking data related to the pandemic in some form. There has been more focused activity with data and analytics regarding COVID-19 than we have ever had with any other disease, including important advances as well as technical and regulatory obstacles.
As the world has adapted to successive waves with different levels of hospitalizations, deaths, and public attention to those levels, many healthcare institutions have slowed investments in data and analytics specific to COVID-19. This now becomes an important time to reflect on the lessons learned with data and analytics in COVID-19. For example, investments in data sharing have changed perceptions of what is possible in terms of models and approaches; shared data have exposed pervasive issues with data quality.
The aim of this research topic is to share the important lessons learned with data and analytics from the COVID-19 pandemic to consider how they may be applied to advance the use of data and analytics in public and population health going forward. Studies should describe:
- The specific challenges in applying analytics during the pandemic
- The impact on healthcare and public health activities
- How the environment in the use of data and analytics has now changed, including widespread adoption of mobile apps, external data sources (location, e.g.), wearables, etc.
- Whether or how the analytics activities have been sustained or applied to other diseases
- Opportunities for improved analytics with data sharing, including technical and regulatory obstacles
- Ethical concerns and lessons learned in the use of data and analytics to drive clinical decision-making
- Approaches for identifying effective interventions and evaluating the impact of these interventions on outcomes
- Lessons learned from failures in approaches using data and analytics, especially if they include discussion or comparison with other more successful approaches and identify recommendations for the future
At the beginning of 2020, as the COVID-19 pandemic swept across the US in multiple waves, health systems had to rapidly develop systems for tracking various aspects related to managing the pandemic. This included not just overall trends in incidence, hospitalizations, and outcomes; but also metrics related to the response.
COVID-19 was the first pandemic in the United States since the widespread adoption of electronic health records incentivized by the Meaningful Use program. As a result, the availability of health information was much broader than in any previous pandemic. The widespread impact of COVID-19 also meant that every healthcare institution was affected, and was tracking data related to the pandemic in some form. There has been more focused activity with data and analytics regarding COVID-19 than we have ever had with any other disease, including important advances as well as technical and regulatory obstacles.
As the world has adapted to successive waves with different levels of hospitalizations, deaths, and public attention to those levels, many healthcare institutions have slowed investments in data and analytics specific to COVID-19. This now becomes an important time to reflect on the lessons learned with data and analytics in COVID-19. For example, investments in data sharing have changed perceptions of what is possible in terms of models and approaches; shared data have exposed pervasive issues with data quality.
The aim of this research topic is to share the important lessons learned with data and analytics from the COVID-19 pandemic to consider how they may be applied to advance the use of data and analytics in public and population health going forward. Studies should describe:
- The specific challenges in applying analytics during the pandemic
- The impact on healthcare and public health activities
- How the environment in the use of data and analytics has now changed, including widespread adoption of mobile apps, external data sources (location, e.g.), wearables, etc.
- Whether or how the analytics activities have been sustained or applied to other diseases
- Opportunities for improved analytics with data sharing, including technical and regulatory obstacles
- Ethical concerns and lessons learned in the use of data and analytics to drive clinical decision-making
- Approaches for identifying effective interventions and evaluating the impact of these interventions on outcomes
- Lessons learned from failures in approaches using data and analytics, especially if they include discussion or comparison with other more successful approaches and identify recommendations for the future
