Skip links

The Value of Data in Healthcare

Originally published in Spanish by eHealth Reporter Latin America
November 11th, 2020

The COVID-19 pandemic evidenced the search for operative efficience through the use of data. However, this issue has been on the table for a long time: Data science allows, among other things, the opportunity to optimize processes and convert health institutions into “Smart Hospitals”; more efficient, sustainable, and with 100% focus on patient experience.

“Data science is a discipline that uses data to extract knowledge and ideas. This is a multi-disciplinary field in continuous development that involves knowledge in business, computational science, and statistics.” Explains Fredi Vivas, Information Systems Engineer, CEO of RockingData, Alumni of Singularity University & Academic Coordinator of the Big Data Program of San Andrés University.

The expert comments that in the last ten years, an explosive combination of greater internet use, greater and cheaper storage capacities, and an increase in interconnected devices caused the volume of data to grow exponentially. “The capacity of a single organization to compete and innovate is closely related to the success they have when analyzing large volumes of data, structured and non-structured, of all origins, be they internal or external. Data-driven organizations (That take decisions based on data) know that data is their principal strategic asset and that it reflects all experience within the organization; this could generate a register within the systems each second all over the world. And all of it can serve to learn”, explains the specialist, highlighting that health data is especially sensitive and for that very reason, must be considered differently than data from other areas.

“Issues like consent forms, assurance of data quality, confidentiality, and legal procedures are, without a doubt, more rigorous. Data encryption, managing them with strict security and guaranteeing transparency throughout the process are also fundamental parts of these projects”, he assures.

In an interview for Healthcareitnews, Professor Mahmood Adil, Medical Director of Public Health Scotland and Digital & Clinical Data Leader of the Edinburgh Royal College of Physicians of Edinburgh, speaks about the current state of affairs in healthcare data of the population and practical ways of integrating them within the healthcare system.

The specialist affirms that three types of data are needed for the improvement of the management of the population’s health: life-style, social determinants, and health services. In general terms, lifestyle represents 30%, health services 25% & the most widely used determinants in healthcare (income, employment, education, household) represent 40% of incidence for the improvement of results in health management.

However, Fredi Vivas sees very little advanced data analysis in healthcare institutions in the current situation “A lot of the time digital information of patients, doctors, & staff of the healthcare centers is in different systems, and that, without a doubt, is very complex because the staff has a very different focus, which is patient care. This makes it impossible to trust the data, which is sometimes unclear in origin, or not of trustworthy quality due to not having a unique point, thus it is difficult to obtain indicators under the same criteria and sometimes this leads to a delay in decision making. Data science allows not only the delivery of better medical care for patients, but the opportunity to optimize processes to convert sanatoriums and hospitals into “Smart Hospitals”, more efficient, sustainable, and hundred percent focused on patient care,” the specialist details.

For his part, Javier Camacho, biomedical engineer and Magister in Management of Technological Innovation Management, states in his “Analytics of big data in health: challenges to overcome”, that “health systems and hospitals have lately become sophisticated, with technology capable of collecting and storing great volumes of patient information. The numbers estimate that each year hospitals store about 1015 bytes of information. In recent times, the application of Big Data has had quite an important role as a support for decision-making processes in medicine.”

Vivas suggests that there are many uses, and that sometimes they imply very personalized projects according to the situation of every medical institution, but that from experience working with machine learning, these are some of its functions:

• Prediction of bed disponibility, closed units, general internment, and shock room.
• Prediction of medical turns throughout the year by analyzing seasonal variables (like holidays and other events).
• Predictions of shift cancellation.
• Real-time staff optimization through a relay board and personnel segmentation (licenses, vacations, highs, extra hours).
• Prediction of bacteriological cultures + correct use of antibiotics, improving patient safety and a reduction of prescription costs.

According to a report by CAEME data analysis is key to identifying and anticipating patient needs. Thus, this allows:

• The Obtention of a centralized and structured archive with all collected data.
• The Segmentation of the population in groups according to similar socio-sanitary characteristics and risk ranges.
• Analyzing the efficiency and adverse effects of a drug or treatment.
• Elaborating statistics about patients to choose those who are better fit for clinical trials.
• Early detection of a patient’s needs in order to provide proper, specialized attention.
• The Obtention of behavioral patterns in patients and health professionals.

On the other hand, the report states that “analyzing historical data along with social impact defines preventive policies: campaigns for environmental and work safety; public policies for the development of health education; probability studies of sickness apparition and continuity; strengthening of vigilance regarding epidemic outbreaks and evaluation of quality in medical services.”

Vivas believes that the COVID-19 pandemic put on the table the search for operative efficiency using data and considers that training on this topic is fundamental “because data empowers the new generation of health professionals, who seek to solve problems using health tech, to be more agile, precise, and safe. From the perspective of medical centers, this can allow them to have smarter and more flexible workspaces, with high-quality standards, that directly impact patient care and the attraction of better talents for work.”

However Professor Mahmood Adil states, in the previous interview, that the development of data alphabetization between health professionals and doctors is very important. “In a world where everything is data, in particular the health sector, data alphabetization in the workforce is very poor in terms of how to access, understand, and use data. We need to improve this so that when we provide the digital tools, they can be used effectively and improving results in the population’s health.”

Find it on