Today, we will dive deep by emphasizing on: healthcare system resilience, the importance of data during crisis and emergency, the right of access to public information vs maintaining data privacy, the recommendations to improve data use during that period. So, let’s find a calm and comfortable seat, and discuss these topics further…

Healthcare system resilience during a crisis and emergency:

The consequences of disaster can affect and put an extra burden on basic human needs such as food, shelter, health, and security in the short term. A long term effect is more visible on education and the economy.

Resilient health care infrastructure is foundational during natural disasters, and pandemics as these crises will increase pressures on the already available system. This resilient system is defined by Jun Rentschler et al. in his report (Frontline: Preparing Healthcare Systems for Shocks from Disasters to Pandemics) as: its capacity to maintain everyday quality of protecting human life and well-being while triumphing, managing and rapidly recovering from the current disaster. 

The importance of data during crisis and emergency:

1. Pre-crisis: data can predict the magnitude of an emergency situation in the healthcare system, and prevents further damage.
2. During crisis: 
   1. A data-driven healthcare system helps support the infrastructure, monitor the resources and reveal the gaps and overlaps between supply and demand during routine and surge health situations. 
   2. It suggests alternative or permanent plans and services and prioritizes the available finances, investments and human resources.
   3. It improves disease analysis, monitors progression, predicts a better action plan and enables effective response; for example, Matthew Robinson (2021) and his colleagues used a computer algorithm to anticipate the sickest patient who needs urgent treatment.
3. Post-crisis: data can assess the efficacy of leadership and management plans that have been executed during the crisis, and can give insightful lessons that can be applied or avoided in the next disaster.

The balance between the right of access to public information vs maintaining data privacy during crisis and emergency:

UNESCO monitors and guarantees human right to access to the information within public authorities that includes the nature of the threat’s progression; the steps and decisions that are taken by the authorities to prevent this progression and protect the public; the emergency allocation of human resources, financial and equipment; and how to get more information.

During an emergency, using this information gives people security, autonomy and accountability when deciding for themselves or their family. Also, it should be accurate and based on evidence, not skewed, and anonymised as privacy protection is another human right.

However, the infrastructure of the data health system has the risk of cyberattacks either by penetrating into the personal patient’s data,or by disrupting health facilities administration systems.

The recommendations to improve data use during crisis and emergency:

Establishing an effective health data infrastructure both nationally and internationally that is based on diversity, ethics, shared values and interoperability. This system should manage the routine pre-crisis demands well before it can withstand the pressure during and post-crisis.

Investing in improving the skills of collecting, analysing and using data. Lessons from misuse of data, either by misinterpretation or violation of privacy and ethics, must be shared internationally.

Infodemic, defined by WHO as the spread of false or lack of information, should be well managed as it can lead to serious issues. It needs collaborative systemic study and evidence-based policies. The false information can be due to misinformation (unintentional) or disinformation (intentional). 

Cyberattacks are continuously emerging. In addition to our need for a resilient healthcare system, we need a resilient health data system that responds to threats routinely and constantly to protect patient data, healthcare system administration, and treatment devices.

The next step:

It is not uncommon for 90% of a company’s or healthcare system’s data to go unused but here comes the tricky part: you never know when you will have to use a tiny part of that 90% in some of your analytics projects. From a purely business and medical perspective, the general rule of thumb is: when in doubt, collect data. Unfortunately, too many data projects fail at the very first step: reliable data collection which is the foundation of all further analytics efforts. Your customers may also ask for verification of certain data even after proper collection. And, you need to organize, audit and clean them before storage to ensure that your data analysis is based on high-quality data. 

You arrive successfully at the appropriate organization that offers you all: data collection, organization, verification, cleaning and maintenance services. If you want to save your time, money and energy, get in touch with us and book your free consultation.

Therefore, it is crucial that we start a healthy discussion on issues such as the reliability of data during COVID & other medical emergencies, the recommendations of the experts in the field; the national academy of medicine, the gaps in data science in healthcare, and the future of data science. Let’s elaborate on these issues…

What is the reliability of data during uncertainty and COVID period?

Reliable data helps in the decision-making process. COVID crisis showed the importance of data science and artificial intelligence in healthcare. But what is equally important is how we collect and use this data. It’s dangerous medically and financially to lower the quality of research to get the most possible quantity. Sure, sometimes, we have to make decisions before getting the complete data. However, the usage of emergency data science can be fast but never rushed.

As Matthew Robinson (2021), an assistant professor of medicine in the Johns Hopkins School of Medicine, said in his article (Data-driven COVID-19 care A new algorithm created by Johns Hopkins scientists predicts which COVID-19 patients will become gravely ill): “IT makes it easier for clinicians to anticipate what will happen to patients and helps them focus on patients who are the sickest”. 

He and his colleagues at the Johns Hopkins University School of Medicine and the Bloomberg School of Public Health innovated SCARP, Severe COVID-19 Adaptive Risk Predictor, a computer algorithm program that alerts clinicians to those who need urgent treatment.

What are the recommendations of the National Academy of Medicine?

The National Academy of Medicine (NAM) is an American Institute of Medicine founded in 1970 and focuses on four strategic action domains—informatics, evidence, financing, and culture. 

To know more about NAM, visit their site: https://nam.edu/

Matheny et al (2019) wrote the NAM key recommendations to successfully execute data science in the healthcare system in his article (Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril):

1. IT governance must manage AI applications and must set the standards of processing and implementing AI. They must enrol in healthcare system leadership and must analyze the financial benefits and risks of implementing AI. 
2. The healthcare administrative leadership must define the short and long-term goals of applying AI to improve the healthcare system. They must be sceptical about the strength and advancement of research.
3. The stakeholders such as governments, patients, consumers and the public must assess the transparency of the healthcare system, and assess the expectation, before operating AI. For example, assessing the intercultural resistance and workflow challenges.

What is the present gap in data science in healthcare?

Data science is a cornerstone in healthcare, but the problem that has been raised many times before, including in K. Rodolfa et al (2021)Taking Our Medicine: Standardizing Data Science Education With Practice at the Core is that there are very few programmed degrees that combine medical training with data science knowledge. It is important to understand that there is a difference between data science, epidemiology and biostatistics. Data scientists have more experience in computer science and informatics, while epidemiologists have a better working knowledge of study design and causal inference.

What is the future of data science in healthcare?

Focusing on training young students in the healthcare industry with specialized health data science degrees can be the current hope.

Over the years, institutions have been innovating to bridge the current gap, and one such developed program that integrates computer science, bioscience, and bioinformatics is innovated by Johns Hopkins University the Krieger School of Arts and Sciences and the Whiting School of Engineering.

Another area to improve in health data science is to create a trusted, flexible rapid response network [E.Kolaczyk et al (2021)]. This network will target three areas:

1. People: the team consists of data scientists, policymakers, emergency leaders and community stakeholders who work together on the same goal.
2. Science: the rapid response science should be based on standards, ethics and coordination
3. Translation: the results of data science must be translated to policies and guidelines with an action plan.

What is the take-home action and solution? 

If you want to know more or have a new idea that needs planning to be implemented, get in touch with us by visiting our Healthcare Analytics page and book your free consultation.