Data: Key Asset for Healthcare Improvement

The fast development and use of many health information technologies has enabled medical organizations to store, share and analyze a large amount of personal health and biomedical data, according to a study by BioMed Research International. With data generation growing every day, the possibilities, risks and challenges of using it in public health increase. However, its correct use is yet to be defined.

Big data collection and analysis enable doctors and health administrators to make more informed decisions about treatment and services, according to Tulane University. In other areas of the healthcare industry, administrators can use key performance indicators and data analytics to make a number of funding and resource allocation decisions. At hospitals and other care facilities, big data can capture a comprehensive picture of the patient experience.

“Big data tools allow care teams to merge data that would otherwise be archived in separate clinics, hospitals and specialist offices and remain underutilized. Instead, big data holds the promise of consolidating patient information, allowing for rapid and accurate communication between patients and providers that draws from a patient’s entire health history,” the university says.

To get the most of this data and its benefits, its correct handling at each step is crucial. The Journal of Big Data (JBD) explains that this can only be achieved by using high-end computing solutions for big data analysis. This is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with the appropriate infrastructure to generate and analyze big data systematically.

Categories of Big Data

There are many categories of health data, the two most common being that generated by the healthcare system and the data generated by the consumer health and wellness industry, according to BioMed Research International.

The first type of data is clinical data recorded by professionals or medical equipment when a patient receives treatment at a hospital or clinic. Clinical data includes electronic health records (EHR), prescriptions, laboratory data, pathology images, radiography and payor claims data.

One of the most successful examples of effective collection and use of clinical data is EHRs, a digital version of a patient’s paper chart. EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users. “While an EHR does contain the medical and treatment history of patients, an EHR system is built to go beyond standard clinical data collected in a provider’s office and can be inclusive of a broader view of a patient’s care,” says Health IT.

EHRs contain a patient’s medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images and laboratory and test results. These allow access to evidence-based tools that providers can use to make decisions about a patient’s care and also automate and streamline providers' workflows.

Having access to all this information reduces logistical and medical errors, making them automatically more cost-effective. At the same time, it allows for greater continuity of care and timely interventions by facilitating communication between multiple healthcare providers and patients. EHRs can also be linked to electronic authorizations and immediate insurance approvals, reducing paperwork.

Consumer health data, on the other hand, can be generated through wearable fitness-tracking devices, medical wearables such as insulin pumps and pacemakers, medical or health monitoring apps and online health services. This information can include breath rates, heart rates, blood pressure, blood glucose levels, walked distance, weight, diet preferences, posture and even data regarding online health consultations.

“This type of nontraditional health-relevant data, often equally revealing of health status, is in widespread commercial use and in the hands of commercial companies. Yet, it is often less accessible to providers, patients and public health institutions for improving individual and population health,” explains BioMed Research International.

Handling Health Big Data

Access to data is being democratized, which means that more and more companies are becoming data-driven. “The goal must be to use this data to drive actions that lead to improved health outcomes – better clinical outcomes, more efficient care delivery or lower healthcare costs,” according to EY. With many players collecting this information, international and national regulatory frameworks are needed to oversee its correct use, respecting the privacy and true ownership of such information.

Health Level 7 (HL7) is one of the most relevant and accepted standards to transfer and share data between various healthcare providers. These standards define and provide formats for messaging and data exchange, decision support, rules syntax and common health data definitions in clinical documents and EHR and personal health record (PHR) claims attachments, quality reporting, product labels for prescription medications and clinical genomics.

The importance of internationally accepted standards, such as HL7 arise from the lack of frameworks in some countries and the desire to unify the process of data sharing at a global level. In Mexico, the latest regulatory reference regarding interoperability and information exchange is NOM-024, issued in 2012. “This means that Mexico is almost 10 years behind in terms of regulation. NOM-024 is not fully compulsory as it does not establish the enforcement of interoperability standards. However, the norm mentions the use of standards such as HL7,” says Victor Medina, President of HL7 Mexico.

Medica explains that theoretically, companies would need to be certified under NOM-024 to use a health information system in Mexico. However, companies could still get the certification even without using HL7 or another standard protocol. Moreover, given the growing volume of new systems in the country following the acceleration of digitalization, the Department of Health Information (DGIS) from the Ministry of Health is over one year behind in answering certification applications.

True Ownership of Data

Hospitals, patients, medical professionals, universities, pharmaceutical companies, biobanks, clinical research organizations and app developers can claim ownership of this data, explains a study by the Journal of Law and Biosciences. “Despite these myriad claims to health data, however, it is just as common to hear doubts about who owns it. Citizens are concerned about potential disenfranchisement, organizations seek the power to reap benefits from their work with data, while health professionals try to follow appropriate principles of data management (of which ownership could be one),” says the study.

The Journal of Law and Biosciences explains that data ownership will depend on each country’s laws and regulations on data protection. However, it recommends each country to first set limits on how and when data can be used and similar issues, including data transparency, accuracy, security and enforceability. There should also be additional guidance from information-focused legal agencies, medical advisory and licensing bodies, supported by lawyers and health professionals, to describe the conditions under which patient information can be disclosed.

Countries like the US do regulate data ownership through the Health Insurance Portability and Accountability Act of 1996 (HIPAA), which contains standards for individuals’ rights to understand and control how their health information is used. “A major goal … is to make sure that individuals’ health information is properly protected, while allowing the flow of health information needed to provide and promote high-quality healthcare and to protect the public’s health and well-being,” explains the CDC.