The Achilles’ Heels of Electronic Health (E-Health) in Southeast Asia: lack of clinical input in health tech products, corruption, and Poor Data Governance

As Dr. Wong of EpiMetrics puts it, “in the Philippines, there is too much ‘E’, and not enough health.”

(See my last post on “Data Driven March Toward Universal Healthcare, https://www.epimetrics.com.ph/) Products are made with little regard to how they would be used or implemented at a hospital. The Philippines has 10 different electronic medical record (EMR) systems. Some hospitals even have two EMRs, and in a study performed by health sciences students at Ataneo de Manila University on two EMRs, the research found one had a 4%, yes four percent, utilization rate. A conversation with Alaya’s Health’s head of informatics cast a ray of hope, however (https://www.achealth.com.ph/). We learned they have implemented Amazon Web Services based HIPAA compliant EMRs, complete with virtual private clouds at their 47 clinics across the country, with very high utilization rates among clinicians because they used those clinicians to help design the electronic medical record.

The biggest challenge by far to e-health adoption lies in one of the Philippines’ greatest pitfalls as a country: corruption. The Philippines is 117th out of 190 countries in the World Bank’s doing business index. A good portion of PhilHealth’s (the organization running the country’s universal healthcare system) 4 billion peso deficit comes not from fraud, but poor management and rampant internal corruption. Employees in a CNN interview described the last chief as “gravely abusive resulting in a grossly mismanaged corporation.” She was fired after six months on the job for extravagant travel and expenditures. Data exposed this and other abuses.

However, even data has not been powerful enough to overthrow the bureaucracy that tried to hide in its ranks. The vice president of informatics at PhilHealth decided to run an internal audit against their own financials, and found a disturbing amount being siphoned away from accounts for two of the Philippines’ most vulnerable groups: the elderly and Class E citizens. The Philippines has five classifications, Class A through E, with Class A being the upper class, and Class E being the lowest socioeconomic class: the 20% of the population living below the poverty line. Data analysis revealed strange patterns of money being removed from these accounts, over time, to the tune of billions of pesos, from the countries’ most vulnerable populations. However, months after the discovery, the VP and his team “voluntarily resigned”, the unwritten code for being forced out by those they had uncovered. Data can help shape and promote efficacious universal healthcare as my last post demonstrates.

As powerful a resource as data may be, in places like the Philippines with rampant corruption, bureaucracies can prove more powerful still.

The last challenge facing e-health implementation is data governance. While much of the healthcare data for PhilHealth may be digitized, it can be very difficult to access, and has few standards. The Department of Health once commissioned a study on fireworks injuries, which the Philippines has thousands of each year. Though the DoH paid for the study, and the data was housed by another DoH a few buildings down, those running the study were never able to get the data from DoH as they followed the rabbit trail. Here, you will often get data with no data dictionary, or a data dictionary but no data. In technical terms, a structured repository of data is called a “data warehouse”.

An IT consultant for health data in the Philippines has called them “data prisons” because of how hard it is to get access to data.

However, even here slow progress is being made through the founding of the Standards and Interoperability Lab for Asia (SILA), the Philippines Health Information Exchange, and data privacy legislation modeled after the European Union’s strict General Data Protection Regulation (GDPR).

As my last post shows, even in a developing country, technology and data can help in the push for more efficacious healthcare, but not without building tech products with the patient and clinicians in mind, and putting in effort to strategize against corruption and poor data governance.