Mass data collection and analysis has become central to cutting-edge technology. Advancing computer tech means we have more ways to collect data, analyze it and derive insights that can lead to breakthroughs or make existing processes more efficient.
The medtech industry will likely continue finding new ways to apply data collection and analytics to medical technology. Here are some of the most significant benefits it’s likely to provide—and some new challenges it may cause for the industry.
Medical Device Data: Benefits
Medical device data has allowed for vast improvements in patient care, leading to better health outcomes. Here are some of the top benefits of connected medical devices.
1. Increased Diagnostic Accuracy
With certain diseases, like breast cancer, the gold-standard method of diagnosis can still have high rates of false positives and negatives. Inaccurate diagnoses can lead doctors to request necessary interventions or delay treatment.
Several new medtech devices and algorithms use AI to analyze scans and patient symptoms. The pattern-finding ability of AI makes it especially effective at detecting subtle differences in a large image—like, for example, a cluster of cells in a CT scan that may indicate cancer.
Some of these devices have been demonstrated to improve the accuracy of diagnostic processes. For example, a new algorithm from Google Health researchers was even better than radiologists at spotting cancer in mammograms.
Other MedTech manufacturers have started to integrate AI directly into their devices themselves. One new CT scanner uses AI to stitch together different images into a more coherent scan and clean up some of the noise that naturally appears on them. The result is a scan that is easier for radiologists to read, which may improve diagnostic accuracy and help reduce the rate of false positives and negatives.
2. Better Symptom Tracking
For some incurable diseases, doctors base treatment plans on symptom severity and progression. In cases like these, the best possible symptom tracking is necessary for helping physicians provide the highest quality of care possible.
However, symptom tracking can often be difficult for patients—as in Parkinson’s disease, where they are expected to take notes on their condition every 30 minutes. Often, patients can’t keep up with this regimen, delaying entries and reducing the accuracy of their self-evaluations. This means doctors don’t have entirely accurate information when building a treatment plan.
Data-tracking wearables can provide higher-quality data. Some researchers have even recommended them as a potential method for symptom-tracking in Parkinson’s.
3. Improved Patient Care
Real-time patient health data can provide many different benefits for doctors.
Wearables and other monitoring devices can enable the use of remote patient monitoring systems, which allow doctors to keep an eye on people even as they move around their hospital or clinic. Physicians who take advantage of these systems can receive instant alerts when a patient’s vitals move beyond a certain threshold, letting them provide the quickest possible response. Other medical systems can help doctors and EMTs collect and review data relevant to patient care in real time, allowing them to work toward improved health outcomes.
Remote patient monitoring systems have been found to reduce the need for follow-up visits and improve patient satisfaction with quality of care.
Cutting back on these visits can be especially beneficial for patients in rural areas, who may need to travel significant distances to receive care. In some cases, an in-person follow-up may not be practical, and remote monitoring can offer a valuable alternative.
Medical Device Data: Challenges
Despite all these benefits, medtech data has some hurdles to overcome, especially around data privacy and security. Here are some areas that need improvement.
1. Untapped and Unstructured Data
MedTech data is collected from a wide variety of sources—including electronic health records (EHRs), genomic sequences, apps, wearables and medical devices.
While this range of sources can be good for doctors and medical researchers, it can also mean that significant amounts of collected data will be challenging to use or analyze.
EHRs, which record patient health information in everyday language, are difficult for traditional computer algorithms to parse. In America, these problems are even worse. Clinician notes here tend to be significantly long — four times longer than those written by doctors in other countries — making it even harder for simple keyword-scanning algorithms to break down EHR data in a way a computer can analyze.
There are also few standards when it comes to the storage and collection of health data. Right now, there’s no guarantee that, for example, two wearables that track patient heart health are looking at the same vitals. Wearables with similar goals may record various types of information or store it differently.
Medical device companies companies that want to use this heterogeneous data for research and development will need to first spend time cleaning and standardizing it into one usable dataset. This process may be difficult or impossible to automate.
Medtech companies will likely need to implement new approaches — like natural language analysis of EHRs — to extract usable information from unstructured data sources.
2. Data Security
Ramping up the collection and storage of information will naturally create new challenges when it comes to data security. Wirelessly transferring info from hard-to-secure wearables to hospital networks, for example, will provide new routes of attack for cybercriminals. The increased storage of patient data will also make hospital networks larger and more valuable targets.
Data breaches and cyberattacks increased across all industries last year. The health care industry was no exception. This trend is expected to continue through 2020 and into the foreseeable future.
MedTech manufacturers will likely need to make security and ethical use of data top priorities in the near future. They should also prepare for new technology—like 5G—that has the potential to create new security vulnerabilities.
3. Ethical Use of Data
At the same time, initiatives like Google’s Project Nightingale have led doctors to raise concerns about the ethical use of medical data.
MedTech companies that use patient and doctors data need to take these ethical concerns seriously. Where possible, they should make sure the right controls are in place to ensure the ethical use of data. They should also make sure that consent to the use and collection of data is collected from both doctors and patients.
The industry will also likely need to start preparing for potential legislation of medical data privacy and ethics. New regulations, like the EU’s GDPR and the Californian CCPA, have begun to require that businesses secure customer consent when collecting their data.
While these regulations are primarily targeted at companies that use data collected from websites, MedTech experts predict that legislation will continue trending toward stricter rules on the collection and use of data. It’s likely that, within the next few years, these laws will begin to have significant impacts on MedTech’s use of data.
How Data Will Help and Hinder MedTech in 2020
In 2020, data will likely become even more central to new medical devices and services. This will be both good and bad news for the industry. Medtech will be able to take advantage of the benefits of medical big data usage, like improved patient health outcomes, better symptom tracking and more accurate diagnostic practices.
However, increased reliance on data will also present a slew of challenges, and force medtech companies to grapple with issues like heterogeneity and privacy. It’s a big-picture issue, and professionals must carefully weigh the pros and cons of medical technology before moving forward.
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