The Benefits of Harnessing the Power of Blockchain in Healthcare

The Benefits of Harnessing the Power of Blockchain in Healthcare

Blockchain is not just about cryptocurrencies and bitcoin. It is about technology which makes it possible to record digital events, creating immutable and distributable data which is secure from any fraudulent manipulation and data breach threats.

Hospitals today are turning to technology to change the way they work, with more efficient healthcare record systems, wearable devices, and medical examination systems implementing artificial intelligence and cryptography.

Blockchain technology, therefore, has interesting use cases in the healthcare domain. As users share the data between networked database systems, a blockchain’s decentralized register of ownership stores all details, starting from the formation of each data block. Its inner SHA256 calculator generates a unique cryptographic hash every time a modification is made to the data, helping to identify the owner of a data block at any time. This fully protected data sharing method is obviously very useful in managing transactions and records in various healthcare systems.

Among other things, blockchain can help in managing patient data and in ensuring drug security in clinical trials and drug traceability. Let’s discuss some of the business and operating model opportunities offered by blockchain for the healthcare industry.

  • Health information exchanges powered by blockchain could help realize the true value of interoperability and integration for disparate health IT systems. Benefits of such HIE’s include reduced costs of current intermediaries, improving efficiencies, and supporting better health outcomes for patients.
  • Irrespective of the volumes of data handled, hospitals can process and store patient data and be assured of data provenance and integrity with blockchain. This protection would extend to patient health information (PHI), electronic health records, data collected from IoT devices (Internet of Things) or monitoring systems and medical insurance claims. When patients need to share their medical records with third parties, each PHI block gets a hash which includes the patient’s ID. Using an API, covered entities receive the necessary information through full or partial access. If the patient is not able to provide or withhold such access, the eligibility to do so may be vested with a reliable third party.
  • Blockchain eliminates the need for a central administrator, offering access security, scalability, and data privacy. It can validate a clinicians’ credentials, control access to patients’ records, secure the medical supply chain and verify clinical tests, without any fear of patient data being mismatched or duplicated.
  • Healthcare data gets anchored to the public blockchain, enabling data integrity to be proven with data timestamps; to authenticate the PHI or clinical research result integrity, allow medical audits and ensure regulatory compliance. Its secure information sharing methods ensure data safety even as healthcare providers and their covered entities provide appropriate medical services.
  • Blockchain does not allow intermediation in data sharing and protects data with more than ordinary encryption, establishing higher levels of data safety when managing insurance claims, PHI, and medical records.
  • Counterfeit drugs are the bane of existence for many in developing countries, while causing losses of over $200 billion to genuine pharma companies in US alone. Timestamped and immutable transactions using blockchain could help track a medicine from the manufacturer to retail and offer the assurance of authenticity and quality needed. It can also restrict access to verified drug dealers and help in detecting all the fraudulent drug dealers.
  • All the statistics, test results, quality reports, etc. generated and recorded during a clinical trial can be made transparent and tamper-free as the whole outcome of the research gets registered and preserved securely in the system.

It is estimated that the use of blockchain in healthcare would grow at a CAGR of 63.85% from 2018 to 2025 and solve all the issues it currently faces with the non-standardized data silos and healthcare data interoperability. With its secure and reliable method of recording, storing, and sharing sensitive data, blockchain can also help prevent data breaches in the healthcare industry. According to a report by BIS research, by 2025, the healthcare industry stands to save up to $100 billion per year by 2025 in data breach-related costs, IT costs, operations costs, support function and personnel costs, counterfeit-related frauds and insurance frauds by turning to blockchain technology. This does not even look at the benefits offered by the snowballing effects of innovation using blockchain.

Data Lakes – The Next Big Thing in Healthcare IT ?

Data Lakes – The Next Big Thing in Healthcare IT ?

In the age when every keystroke on your keyboard or swipe on your phone is tracked the era of Big Data is thriving. The advent of Microsoft Azure in 2008 allowed the Healthcare Industry to finally have access  information that, up until that point, had only been accessible via large companies such as IBM. The ability for the Healthcare Industry to pull information based on mass amounts of accurate data was nothing short of revolutionary.

The advent of this mammoth data machine altered the face of both the for-profit and non-profit sector.  It changed the way nearly all organizations worked and created entirely new industries. With the addition and popularity of mobile applications in the late 2000’s the business of tracking data all but exploded. Soon preventative health was being tackled by companies such as Fitbit which created a personal activity tracker which measures and tracks heart rate, sleep activity and number of steps walked.

Data Lakes - The Next Big Thing

The flood of data coming in, literally, from all corners of the world was organized into countless institutional Data Warehouses. Early industry predictors indicated that this mass amount of data would lead to healthcare researchers quickly uncovering information that could lead to cures or treatments. While this newfound data assisted greatly, flaws in the Data Warehouse concept were soon discovered.

The modern concept of the Data Warehouse began in the late 1980’s. IBM’s Systems Journal article published in 1988 coined the term “business data warehouse”. Bill Inmon (the ‘father’ of data warehousing) began to discuss Data Warehouses as far back as the 1970’s and in the early 1990’s published the industry bible Building the Data Warehouse. Inmon’s model for data warehousing concentrates on a centralized data repository.

Healthcare providers and researchers began to realize that this model meant accessing the data proved much more difficult and often it was not helpful to their research.  The main issue they faced was that the Data Warehouses were designed and controlled by a diverse range of operators. These individual operators could range from hospitals to research centres. These Data Warehouses employed the concept of ‘schema on write’, meaning that the data is organized as it is added to the warehouse. In fact, data is not even loaded until its eventual use is determined. For healthcare providers and researchers this method meant that they had to rely on countless institutions and their respective warehouse designs.  The information culled from disparate Data Warehouses produced at times inconsistent and conflicting data. Also, the ‘schema on write’ method prevented data from being entered in a timely manner; all information would first have to be surveyed and analyzed through individual systems. Healthcare leaders realized what they needed was access to unstructured data that they could analyze on their own timeline.

The concept of Data Lakes was born.

Data Lakes - DapasoftA Data Lake is a storage system that is able to hold mass amounts of data, but unlike the Data Warehouse with its structured, hierarchical format, the Data Lake holds raw data intentionally eschewing up-front formatting to provide users unfiltered access to the most up to date information. Data Lakes use the concept of ‘schema on read’; data is not analyzed until the end-user accesses it.

Therefore, with Data Lakes at their disposal the Healthcare Industry are not constrained by institutional schemas. While it is logical that hospitals worldwide have created their own Data Warehouses based on their own understanding of what was required by the front-end user, naturally each institutional Data Warehouse would be managed by different teams of people whose intake process for the Data Warehouse can inherently cause wide gulfs in how information is analyzed. In contrast, the Data Lake allows users to pull raw healthcare data unburdened by (if well meaning) ineffective filters.

Data Lakes provide numerous advantages over Data Warehouses for the Healthcare Industry beyond data capture.

Healthcare spending in Canada now runs into the billions of dollars annually. A portion of this cost is infrastructure spending to operate Canadian healthcare institutions including their IT operations and data storage. Adopting the use of Data Lakes greatly minimizes the costs associated with data capture and storage. Not only do operators save costs on the physical assets required for storage, but they can avoid the cost of hiring specialized staff for schematic design and data input.

Data Lakes also allows practitioners to provide patients with Precision Medicine.  Precision Medicine is an emerging medical concept that proposes tailoring healthcare to individual patients. Using Data Lakes and previously mentioned health applications such as the Fitbit personal health tracker, the ability for capturing unfiltered health information from individuals and its timely analysis can now have immediate impact for patients. By its very definition, Data Lakes provide the most open, agile format for end users.

The Healthcare Industry can now take advantage of Data Lakes supported by Microsoft Azure.

Azure Data Lakes will enable the Healthcare Industry to create repositories where their data can be held without constraint. Data of any size or format can be held at a much lower cost, and these savings can be used toward providing improved patient care. Health practitioners and researchers can also access data in real-time increasing the speed in which to apply this knowledge to produce real-world results. The Azure Data Lakes also enable users to invest in new technology without concern that this investment will not sync with their current Data Warehouse.

Big Data provided the Healthcare Industry volumes of structured information that influenced practitioners and researchers alike.  Azure Data Lakes is the bold next step and the future of Healthcare Data.

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App and Data Integration Trends in Healthcare

App and Data Integration Trends in Healthcare

The US healthcare market is fast evolving from a pay for service model to pay for performance/outcome model. New legislations such as MACRA, MIPs, and Meaningful Use has accelerated this evolution recently.  MACRA specifically calls for identifying, measuring, and reporting treatment outcomes based on clinical procedures. Therefore it very pertinent to have relevant app and data integration for healthcare providers.

In this infographic, we look at the state of data and app integration in healthcare.

App and Data Integration Trends in Healthcare

Infographic showing App and Data Integration Trends in healthcare

App and Data Integration Trends in healthcare