The criticality of clean and actionable data to the healthcare value chain cannot ever be overemphasized – whether for clinicians, support departments, administrators, service providers, or patients. The magnitude and multiplicity of data pose a serious challenge to its interoperability and measurable value in terms of patient outcomes.
Given the intricate maze of disparate data points, including online and offline consulting interactions, nursing records, pharmacy transactions, hospital administration procedures, diagnostic lab reports, research and development initiatives among other sources, data collection from disparate databases is nothing short a herculean task. Data cleansing and integration is hence a top priority for healthcare.
The post-pandemic world has changed the healthcare dynamic in unprecedented ways. The Covid-19 pandemic, through its virulent transmission, has dismissed all prevalent notions of possibility and impossibility in one stroke. Left with no option, the most disruptive of all innovations, as unprecedented as the virus that triggered it, has happened in the healthcare space: an incredible shrinkage in Bench-to-Bedside progression. What was typically a laboratory contemplation of several years was reduced to a matter of few months.
The virus has singularly urged healthcare and pharma companies to keep pace with the industry in terms of technological adoption encompassing AI, robotics, digital therapies, IoTs, wearable devices, big data, blockchain and nano health. The consequent implication on data management is mind boggling to say the least. Among other things, there is an urgent need to standardize data formats in line with FHIR recommendations besides enhancing data storage and retrieval capabilities and ensuring scrupulous compliance with data privacy norms.
So, how should healthcare organizations go about cleaning the diverse data coming from disparate sources and ensuring its seamless integration for ensuring solution-centric cross-talk between different applications and generating highly actionable and easily accessible insights.
Before we emphatically declare data lakes and cloud storage as the proverbial silver bullets, it is important to set up a multi-disciplinary data management team in the organization with commensurate representation from key stakeholders including data scientists, software and hardware experts, clinicians, research heads, lab specialists, strategy team, patient-facing staff, and administration executives.
We need meticulous data curation to enhance the quality of analytics. The exchange of clean data is critical for the velocity, volume, veracity, variety, variability, visualization and value of big data and supercomputer-powered analytics, yielding sustainable and measurable outcomes in mission-critical areas. Given clean and actionable data, healthcare will be able to make the most of data science capabilities, thereby enhancing patient care and optimising organizational processes.
Technology will prove a game changer, only if the data management team holistically creates a mega data store to go hand in hand with API-centric architectures. A collaborative effort can alone help create a holistic framework for the scrupulous and seamless and responsible management of healthcare information through data democratization, integrity, and interoperability.
