Thanks to advances in digital healthcare, it is now possible to get a medical check-up without setting foot in a hospital or doctor’s office. However, when it comes to getting diagnostic tests done, not much has changed. It’s true that digital healthcare providers and innovators are striving to make testing and diagnostics conductible over digital mediums by using devices like smartphones and wearable devices as point-of-care testing tools. But it leads us to wonder, how accurate are the results?
Traditional diagnostic techniques prove that these devices aren’t always 100% accurate. In fact, the efficacy of current diagnostic practices is being questioned on the grounds of accuracy. There is concern about whether or not the results generated by these devices are showing us what we really need to see. For instance, for many years the pulmonary artery catheter (PAC) has been the standard practice for obtaining the data required for optimal care and treatment of critically-ill patients. Until it was recently discovered that “there is no evidence that the use of the PAC has improved patients outcomes.”
Can advances in “machine learning” help improve medical diagnoses? At least one company says it can, and they have joined forces with hospitals and medical imaging companies to obtain data from a range of different sources: X-rays, lab results, doctor’s notes, and claims records. The company is developing software algorithms that can recognize otherwise imperceptible patterns in these data sources, enabling closer-to-real diagnoses.
Recent studies have also found, perhaps not surprisingly, that Americans often share more accurate health-related information on social media channels than they ever do with their doctors, insurers, or government agencies.
Social media use has really taken over when it some stop how people communicate and share personal information, and the above report is another indicator of how important all of this digital “big data” could be used to help improve diagnoses and lead to better overall health care. For example:
Diseases may be detected at an early stage. The collection of more accurate data sets could allow for better diagnostics at much earlier stages of the lifecycle of an illness. This would result in more effective treatment and faster recovery. It can be especially useful in cases of critical diseases such as cancer and diabetes, where early detection can have a tremendous impact on the efficacy of treatment and the longevity of the patient.
Clinical trials could be conducted faster. Clinical trials are normally time-intensive and extremely expensive, but data analytics can help to make the process faster and more cost-effective. Big data could reduce the time and resources required to carry out clinical trials and research, therefore lowering the overall cost significantly.
Scope of diagnosis may be increased. Big data tools and software could help doctors expand their scope of diagnosis. Data analytics software used to compare and process medical records of patients with similar symptoms, habits, and demographic details could lead to more accurate diagnostic results. Not only would that lead to a better course of treatment, but it would also eliminate the need to conduct multiple, unnecessary tests. Accurate diagnosis with less testing would lead to an overall reduction in medical expenses for patients, and lighten the load on the entire medical system.
Treatment can be personalized. Oftentimes, patients suffering from similar conditions may need different approaches in treatment due to medical or genetic history, previous reactions to a particular treatment or medications, or a host of other factors. Data analytics can help medical practitioners and caregivers quickly and more accurately identify the medicines and treatment processes that will be both safe and effective for each individual patient.
The use of better critical data streams will not just improve the treatment outcomes and overall experience for patients, it will also pave the way for more evidence-based and preventive diagnostic methods, and help reduce the stress that our aging population is poised to put on the healthcare system as a whole.
What do you think? Would you share your patients’ data with others? What if you were on the developers side? Would it harm your business in any way to make the data your device or app gathers available to everyone? And what about privacy issues? We would love to hear your thoughts.