Connecting the data supply chain concept with a world-renowned detective seems outlandish. But do you remember Sherlock Holmes saying, “It is a capital mistake to theorize before when you have all the evidence (data). It biases the judgment.”? And, “there are fifty who can reason synthetically for one who reasons analytically” in A Study in Scarlet published in 1887? There is more here in common than one might think at first glance.
It is incontrovertibly true that better judgment or decision-making is possible with an accurate set of supporting data or information. Be it genetic algorithm (GA), artificial intelligence (AI), or any other technology, data remains critical to its optimal utilization. A strong data supply chain in itself is a statement of a strong information value chain.
Healthcare is among the most regulated sectors that generate a humongous amount of data related to patient management, administration, testing, clinical and claim management, and other financial processes. Globally, it is expected that healthcare data will grow at least 25000 petabytes by 2020. Much of this data is captured in hard copies and manually fed into systems in the form of admission, declarations, health assessments, and evaluation forms, among other procedures. In the entire process, data duplication and typographical errors are unavoidable human mistakes. These lead to bigger questions related to data governance, integrity, and ownership. A study reveals that as many as 56% of healthcare providers do not have a proper data governance system in place.
Moreover, healthcare system data typically lies scattered in disparate systems. Making it easily available for physicians, patients, or administrative personnel for any critical task continues to be a challenge. As a result, there lie several weak links in heterogeneous healthcare systems that hinder the uninterrupted flow of data.
Nonetheless, regulatory standards like HIPPA (Health Insurance Portability and Accountability Act) play a vital role in system integration and data sharing, making it imperative for healthcare organizations to secure patient-sensitive data. Healthcare institutions must be mindful of the cost of non-adherence to HIPPA, which is generally very high. It is one of the reasons why healthcare systems operate in closed-loop models.
Connecting the Unconnected: Healthcare Systems
Unlike in the US where 90% of healthcare organizations use Electronic Health Records (EHR), India continues to be in a very nascent state. The trend towards increasing deployment of digital technologies is helping increase the accuracy of healthcare records. To connect the data points in a supply chain and build a real-time and secured healthcare system, organizations are embracing:
· Digital Data Collection: Data collection through web-based applications or online operating systems is likely to reduce errors in health records. Capturing data through smart and secured devices like tablets, smartphones, or any handheld devices can directly send the data to a central repository, making it available for further usage.
· Smart Cards: Using smart cards for patients to reduce recurring data entry and build a smart Hospital Information System (HIS).
· Cloud-based Applications and Services: Cloud is a cost-effective solution that facilitates data storage, easy manageability, disaster recovery, flexibility, and scalability. As a result, most healthcare service providers are migrating to cloud-based applications and services. But the cloud security like data control over access, transmission security, authentication protocol, integrity, and auditing has to be taken care of. These are data security measures in cloud-based systems. Cloud systems can be beneficial only when we take appropriate security measures for data sharing.
· Data Security Measures: To protect organizational data, healthcare institutions have to reinforce security by deploying updated antivirus solutions, setting up firewalls, encrypting sensitive data, and implementing multifactor authentication (MFA).
· Seamless System Integration: Surgical and medical research is currently suffering due to the difficulty in building large datasets across existing silos of patient data. Instead of adopting a non-integrated custom software, healthcare organizations should implement an enterprise-wide integrated, secure, and scalable solution.
· HIPAA Standards: It is imperative to build and manage a system compliant to the regulatory standards to strike a balance between the closed-loop system (that cannot communicate with other systems because of technical challenges and security protocols) and data sharing. If such a balance is accomplished, we can create a system, which can share the data without any technical challenge and with proper security.
· Ownership: Assigning data ownership for entire data supply chain will enhance data authenticity and improve data governance. Clean and refined data facilitates research and decision making.
· Advanced Analytics: Turning data into strategic insights is one of the primary objectives of all healthcare organizations. This can be achieved by deploying cutting-edge technologies such as AI, machine learning, and robotic process automation (RPA).
The data supply chain has tremendous potential to accelerate advanced scientific research and establish dynamic enterprise analytics. The power of the data will be appreciated more if concurrent systems are replaced by an integrated, secured, and digitally connected healthcare infrastructure, because only a smart data supply chain can help develop a smart healthcare system.