The following is a guest blog post by Rahul Varshneya, Co-Founder and President of Arkenea and Benchpoint.
The healthcare industry is rapidly incorporating changes in technology. There is a gradual shift from the service based model of healthcare which primarily focused on curing the ailments, to a more holistic outcome-based approach which not only involves exploring different treatment modalities but actually aims at understanding the causative factors behind various ailments and eliminating them.
There has been a significant increase in health data, both structured and unstructured that is being generated. The high complexity level of this data requires it’s processing by big data analytics to come up with relevant and applicable inferences. Telemedicine is also harnessing the power of big data to improve the existing healthcare facilities.
The market size for Telemedicine is expected to increase to 41.2 billion dollars by 2021 and Big Data analytics is going to play a major role in this surge.
Here are the ways in which Big Data is contributing to the advancement of Telemedicine.
1. Patient Health Tracking and Predictive Analytics
The biggest benefit of the application of Big data in Telemedicine is the identification of potential health problems before their transformation into aggravating conditions. This has become a reality with the advent of the Internet of Medical Things (IoMT) in the form of wearable fitness trackers and other wearable health monitors which collect patient data on a real-time basis.
Application of Big data analytics techniques to this data ensures that patients vitals and statistics are constantly monitored. Telemedicine facilitates regular interactions with healthcare professionals without having to visit the doctor’s office. It also ensures that the physicians are constantly updated about the patient’s health status resulting in early detection of any anomaly.
The historical data collected is used for predictive analytics of the possible future outcomes. Creating risk scores on the basis of data from various sources is important for the identification of individuals at elevated risks of developing chronic ailments at the early stage of disease progression.
2. Remote Patient Monitoring and Post Discharge Prophylaxis
Post-discharge monitoring of patients and appointments with the physicians via telemedicine saves unnecessary visits to the doctor’s clinic. This is also a boon in the case of elderly and debilitated patients who cannot make frequent trips to the hospital for regular checkups. Vital patient stats like blood pressure and heart rate are collected by the use of health devices which have advanced sensors attached to them.
The data collected is processed using analytics techniques to compute the effective dosage of medication to be administered and helps the physician decide the course of treatment to be prescribed.
The clinicians are able to make use of numerous healthcare based apps to remotely monitor the patient condition and be on the lookout for signs of disease progression. This helps keeping the patients out of the hospitals, ensures that healthcare providers’ effort are focused on caring for patients in critical condition and also keeps the cost of healthcare relatively low by avoiding unnecessary hospitalization.
3. Accurate Diagnosis and Precision Medicine
Historically, the diagnostic process relied solely on patients relaying the symptoms to the doctor and doctor noticing the clinical signs of disease. The tests ordered further confirmed the doctor’s diagnosis and a treatment plan was prescribed. Now instead of subjective symptoms reported by the patient, the doctors can base their diagnosis on the patient data collected regularly by the wearable devices. Furthermore, the benefit of telemedicine is that the doctor and the patient don’t even need to be in the same geographical location for the diagnosis to take place!
Application of Big data in Telemedicine not only results in a more accurate diagnosis, but it also is a giant leap from traditional generic medicine into the realm of precision medicine curated specifically for each individual. The data collected from patients’ wearable devices, healthcare based apps, patients’ electronic health records, and genomics data can be tapped into for developing a medication that caters to patients individually.
Precision medicine takes into account the variation in lifestyles, genetic makeup, and environmental conditions for each individual. Big data makes it possible to compute the relevant data collected from various sources and helps the healthcare professionals come up with a treatment plan specific to each individual.
4. Cloud Computing and Specialist Outreach
The sheer volume of health data generated has led to the storage of patients EHRs and EMRs on the cloud. The benefit of telemedicine is that the patient data can be remotely accessed and treatment can be prescribed irrespective of the geographical location of the patient and the healthcare provider. It is of great advantage in case of a referral to the specialist who may be at a different location than the patient.
Secure access to the cloud ensures that physical location is no longer a variable in availing the best treatment possible. It is also beneficial to the healthcare providers as it allows for better scheduling of the doctor’s time increasing the effectiveness of care. Cloud storage is a precursor to the emergence of big data and acts as its facilitator.
5. Predicting Infection Trends and Timely Interventions
Application of deep learning algorithms across healthcare related data can be instrumental for prediction of infectious diseases and studying the patterns and trends of infection spread. The importance of data-based infectious disease surveillance studies has been recognized by a number of researchers across the world. These studies are important for supplementing the existing systems and designing of newer models of disease progression.
Big data in the form of Internet search queries are also being utilized for understanding disease trends, predicting the spread of infectious diseases. Once the regions affected by the infection are identified, the benefits of telemedicine come to light. Physician interactions with the affected populations and deployment of treatment modalities to the infected patients by use of tools like teleconferencing result in timely intervention and prevent the further spread of infection.
Conclusion
Big data analytics gives the physicians access to massive volumes of information which increases the diagnostic accuracy and results in efficiency in healthcare delivery. Combining the power of Telehealth with Big data has the potential to transform the healthcare delivery system and is of immense benefit to both the patients as well as healthcare providers.
Data security and privacy concerns are the biggest threats to this advancements. Enforcement of appropriate security measures need to ensured so that the vast reservoir of healthcare data can be harnessed to its full potential.
About Rahul Varshneya
Rahul Varshneya is the co-founder and President of Arkenea and Benchpoint. Rahul has been featured as a business technology thought leader in numerous media channels such as Bloomberg TV, Forbes, HuffPost, Inc, among others.
