Moving Toward a More Perfect Union of Patient Data Collection

medical provider looking at patient data on a tablet

As many as four out of five patients lie to their doctors, or at least tell them half-truths, according to this study (it builds on a trend noted almost a decade earlier). The digitization of their data, along with the digitalization of patient access, could help encourage them to tell the truth and backup accuracy when they do not. It could even tell things they did not know were relevant enough to share. It’s time we move toward a more perfect union of patient data collection.

Undisclosed means unknown

The reasons for lying to doctors are many and shouldn’t be surprising to any of us. People don’t like revealing facts about themselves that suggest failures of ability or will, like keeping to diets or making other lifestyle choices. Many people never even get to that point, preferring instead to avoid interaction with healthcare providers because of cultural traditions that discourage asking for help.

Then there’s the simple fact that people are imperfect data gatherers and reporters (as evidenced by any number of detailed guides to help patients prepare for their visits, such as this one). So, while not an error of purposeful commission, the omission of facts that might seem unrelated or inconsequential mean that doctors are impeded in finding another clue or even the missing key to a better diagnosis or an improved care regime. We need a better culture of patient data collection in the healthcare setting.

These traditional shortcomings or errors in understanding patient conditions will only make it harder for our industry to meet the care needs of “long-haul” post-COVID-19 patients while moving to value-based care models.

Data is truth agnostic

Data is either part of the solution to this conundrum or, if not embraced strategically, part of the problem.

The digitization and digitalization of healthcare records, treatment, and patient access have been underway for years, with much of that imperfect information being passed onto new systems and then comingled with newer patient records that are incomplete, inaccurate, or duplicates. It was estimated a few years ago that at least one-third of the data in patient electronic records were impaired, contributing to an average added cost of $800 for ER visits, $1,950 per patient for every inpatient stay, and $1,100 per patient for repeat care.

COVID-19 has accelerated this process, bringing more files into the digital realm along with more errors of commission or omission. Early in the pandemic, it was estimated that 1,800 duplicate records were being created every 24 hours. Now, multiply that by the number of adults migrating to online portals for remote access to healthcare now and in the future and those numbers could be measured in the tens if not hundreds of millions.

Equipping doctors for success

It might prove far easier to ensure that data do not lie instead of policing patients.

Behavioral data are demographic and lifestyle touchpoints that accrue from real experience and are collected by smartphones in the normal course of use. As such, they shed light on habits and routines that are evidenced by actions, not assertions or opinions, and can provide the basis for insights into correlated conditions, if not outright causes.

Combining behavioral and socio-economic data with clinical data can unlock powerful insights. For instance, in the early days of the pandemic, knowing individuals with pre-existing health conditions, over the age of 65, living in densely populated living quarters and actively using public transportation could have helped better identify people most susceptible to contracting severe COVID cases. Or imagine if doctors know which patients are experiencing financial hardships that affect their ability to buy insulin or another necessary treatment. Those patient-doctor interactions, whether in-person or remotely, would be dramatically different. Patient data collection is so key nowadays.

Add to this data the output from health-related wearables – which are expected to number over 1.1 billion next year – and there is a vast and ever-increasing amount and variety of informational inputs being generated that can contribute to providing more holistic and reliably effective treatments.

This data can’t lie.

We can learn from other industries

Collecting and putting to use diverse data streams is an established practice in the financial services world and embodied in spirit if no detail in related businesses like real estate; just think of the amount of diverse data that are available to the average homebuyer, not to mention the detailed credit risk profiles that help determine financing, insurance risk, and other attributes or conditions.

No institution would rely on information from customers alone, as they know they wouldn’t be getting the full picture (and thereby an accurate one, lying notwithstanding). We have been providing such integrated insights in the credit business for years.

Why not import some or much of this approach to healthcare? Looking outside the four walls of a doctor’s office might reveal a host of readily available data that can be used to improve the understanding of patients while respecting all privacy requirements. A multipronged strategy might combine clinical data, patient surveys, and external behavioral and wearable sources as supplements. This approach could also yield a united and harmonious arrangement of data that addresses omissions and imperfections.

The likelihood that human nature is going to change is just shy of nil, and the challenges of collecting and cleansing existing patient data is an ongoing, if not surmountable task.

Patient data collection

But perhaps the question we should be asking isn’t “why do patients lie” but rather “why don’t we start augmenting our work with data that does not?” It’s time that we move toward a more perfect union of patient data collection.


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