Medical reporting is an integral part of the medical industry (i.e. Medical Billing Services), but a lack of standardized reporting techniques makes the field more complicated than it needs to be.

In addition to making it more difficult to care for patients, this has caused the healthcare field to lag behind many other areas that use similar technology.

Networked medical systems have their share of risks — any networked system can be susceptible to hacking if the hardware or software isn’t upgraded and secured.

Emerging technology could help to standardize medical reporting and make it more secure. What modern technologies can we look forward to in the future regarding standardized medical reporting? Here are four of them.

1. EHR Adoption

Electronic health records (EHRs) are standard equipment in most practices, but that wasn’t always the case. The concept of electronic medical records was introduced in the 1950s, but until personal computers became more affordable and more functional, the implementation wasn’t as practical as it could be.

In 1991, the Institute of Medicine declared that all physicians should be using a computer to improve patient care by 2000, but that didn’t provide any specifications for record standardizations.

It wasn’t until 2014 that the American Recovery and Reinvestment Act offered incentives to physicians to encourage them to adopt EHRs in their daily practice.

2. Diagnostic Standards

If you ask 300 different radiologists to describe a normal looking thyroid on a CT scan, you’d likely get 300 different answers. However, tech is helping to standardize such discrepancies:

“Recent technology developments are helping to reduce variations in radiology reporting language,” says Richard Morris of Healthcare Administrative Partners, noting that the issue has persisted for many years.

“Using data from dictated reports,” he says, “analytics processes are available right now to help radiologists increase standardization and to improve patient care. Adopting these processes today will help prepare practices to take advantage of new technologies on the horizon that use machine learning, such as advanced natural language processing software.”

While another medical professional might be able to puzzle out the information contained in a patient’s EHR, it can be difficult for insurance companies to understand, and downright impossible for patients who review their health files. Even referring physicians might have some trouble interrupting the information.

The use of artificial intelligence can help make these various diagnoses easier to understand.  A program called Deep Dive Analytics uses machine learning algorithms to study the predictable variations in human language to generate a concise and easy to understand diagnosis – something that even human medical transcriptionists can’t always do.

This application of artificial intelligence is still in its infancy, but it could change the way medical diagnoses are processed, making them more standardized and improving patient care in the long run.

3. Better Treatment

There are dozens, if not hundreds, of different FDA-approved cancer drugs, and finding the right combination of these drugs for each cancer patient is often a case of trial and error. If you remember basic middle school math, just 100 drugs can create more than 5000 combinations — and that’s only if the patient is prescribed two drugs.

Artificial intelligence and machine learning algorithms can be used to predict which medications will work best for each type of cancer, using information about each drug. This is something that can be difficult or even impossible for individual researchers to do — not only are there a nearly insurmountable number of possible drug combinations but switching drugs mid-treatment can also be detrimental to a patient’s health.

Not only could this change the way we treat cancer, but it could also help improve patient outcomes by allowing them and their doctors to skip the typical ‘trial and error’ that usually accompanies cancer treatment and finding the best medication. It could even help to standardize medical reporting by taking the guesswork out of picking the best treatments.

4. Improved Quality of Life

Patients don’t want to think about their mortality, but it is something medical professionals have to think about on a regular basis — especially when helping patients choose the best treatment options.

Predictive algorithms, when paired with patient history, have been able to predict a patient’s life expectancy accurately and determine whether the patient is likely to pass away within the next five years.

It might sound a bit morbid, but this information could be invaluable for medical professionals to help them determine the best treatment for patients who may not survive beyond that five-year period. If a patient would benefit more from a quality-of-life treatment than from a harsher treatment that might only have a small chance of extending their life, most would likely choose to take that route.

Having this kind of information could make it easier to standardize medical reporting, as well. Life expectancy information could help professionals by preventing unnecessary and potentially expensive treatments.

Technology is developing faster than we can keep up with sometimes, and the changes happening in the medical field are altering the way professionals manage their medical records. These applications are just starting to come about, but these first steps will likely shape the future of the medical field.

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Kayla Matthews is a Pittsburgh native with a passion for personal productivity, business efficiency, and technological advances in the healthcare field. She's written for various influential publications such as Medical Economics, Contagion Live, HITECH Answers, and CareCloud. To see more of her bylines, please visit her LinkedIn profile or read her tech productivity blog on WriterZone and Productivity Bytes.

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