The healthcare data has got enormous potential which lies dormant in unfathomed silos. It can be harnessed with the help of analytics to understand diseases, develop treatments, and improve therapeutics & clinical research. Enterprises can transform a reactive healthcare system into a continuous and proactive healthcare system. They can solve many medical problems with better evidence-based decision making, insights, and information processing. However, the success rate in this paradigm is hindered by some fundamental integration challenges.
Fascinating Challenges before Behavioural Health
The computational power is doubling after every year in the healthcare industry but the data governance systems are still set in the 1800s in old silos. High throughput technologies like genome sequencing systems, imaging sensors, mobile, and internet technologies are generating enormous quantities of data. In many enterprises, this data is kept in old boxes and silos for a long time.
Functional silos make it difficult to process in high-dimensional, semi-structured, and heterogeneous format. Visualizing the collected data becomes a grand challenge for healthcare enterprises. The collected data cannot be stored for distributed computing or new statistical or computational methods. Moreover, it can’t be fetched into an Excel or comprehended.
Another restraint is the lack of ability to study things at scale. Precision in Big Data technologies comes with size and scale. Researchers should be able to find the same kind of people with the same kind of problems. The insights from 50,000 people will be much better than the insights collected from 5000 people. The level of details and insights can help researchers in drawing meaningful interpretations. Without size and variety of data, there is huge potential to draw wrong conclusions as data sets will not be big enough for considering different factors and establishing cause and effect relationships.
Eliminating Drags with a Drift-to-Drive Data Integration Approach
Healthcare organizations can climb these challenges with a multi-model that connects multiple healthcare systems, insurers, researchers, and producers for the seamless movement of data into the analytics production line. It can enable researchers to capture vast amounts of patients over a large timescale. Data stemming from a myriad of clinical and non-clinical sources can be structured for meaningful interpretation. This can enable researchers or doctors to view a patient’s physiological conditions from a broader viewpoint. They can segregate the data sets to find patterns and accuracy of diagnosis. Multi-institutional data sharing becomes fast & smooth for retrospective and real-time analysis.
Data Integration and Infinite Possibilities in Healthcare
The Uber use case has a lot of relevance for the healthcare industry as well. Uber has redefined how we book our travel in terms of time, cost, and engagement. Uber’s key to success resides in layering together maps, GPS, tracking systems, payment systems, and taxi service providers into a robust transportation network. Similar layering of digital diagnostics, researchers, and doctors can improve every aspect of healthcare and transform tech devices into new hospitals.
In this way, sick care can become cost-effective and hassle-free. Uninsured and underinsured consumers can search and find a medical professional, based on specialty at an effective price. Patients don’t have to travel miles, go through lengthy questionnaires, or take a break from work for consultation. Affordable, accessible, and available care on mobile devices might prevent diseases from becoming chronic. Moreover, doctors can make use of advanced sensors to track patients activities related to diet, exercise, posture, mindfulness, etc. and tailor chronic disease management plans. This advantage can dramatically bring down the cost of sick care.Here are some other medical science areas which can be improved with integration:
- 1.Research & development: Drug discovery can be accelerated with predictive modeling to build a leaner, agile, and targeted R & D pipeline. Statistical tools and algorithms can improve trials and patient recruitments with reduced error rates. Adverse effects of drugs can be discovered earlier before they reach the market.
- 2.Public health: Integration can streamline public health surveillance and reduce response time for dealing with outbreaks or epidemics. Countries can prevent crisis for the benefit of populations.
- 3.Evidence-based medicine: A variety of structured and unstructured data-EMRs, financial and operational data, clinical data, and genomic data can be studied for delivering effective care.
- 4.Genomic analytics: Researchers get the ability to execute gene sequencing more efficiently and cost-effectively.
- 5.Patient profile analytics: Advanced analytics of patient profiles can identify individuals who would benefit from proactive care or lifestyle changes. Teams will get the ability to capture and analyze large volumes of fast-moving data from, for safety monitoring and adverse event prediction.
Enterprises like Lemonaid Health are doing all this successfully through an app that provides health services for $25. Doctors at remote places are making use of health-related applications and digital health tools to issue a prescription just after few minutes of diagnosis. Another notable name in this domain is HealthTap, which provides on-demand access to physicians at an affordable price. The organization has over 107,000 freelance physicians across the United Statesto treat patients on demand.
Health data can be harnessed effectively with the help of advanced information sciences to improve every aspect of healthcare. By gleaning insights, researchers and physicians can focus on best treatment for patients. However, to do this smoothly they will require start-to-end integration between high throughput healthcare technologies, i.e., mobile trackers, mobile devices, sensors, etc. for optimizing results and innovating without constraints.