The following is a guest article by Pranay Jain, CEO and Co-founder of Enterprise Bot.
The Health Insurance Portability and Accountability Act (HIPAA) was introduced in 1996 mainly to help migrate insurance coverage details of employees between organizations, prevent insurance fraud, reduce wastage of medical resources and eliminate improper payments.
In a study by the Centers for Medicare and Medicaid Services (CMS), it was observed that over 8% of all healthcare payments were improper in 2018 alone.
To reduce all forms of erroneous payments, CMS introduced a testing practice known as Comprehensive Error Rate Testing (CERT). The methodology employs engineered AI algorithms and Predictive Analytics to detect fraudulent healthcare payments and other forms of payment-related errors. By doing so, the United States government has saved around USD 42 Billion in total.
Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) have made massive strides across the patient journey by improving R&D processes for Drug Discovery, eliminating errors in Drug Administration, and revolutionizing the world of Diagnostics/Medical Prognosis. By analyzing clinical data and physician notes across Electronic Health Records (EHRs), the quality of healthcare imparted has seismically elevated into a new era of patient care management.
However, the entire Patient Care Management (PCM) journey involves a list of backend operations across healthcare establishments. To avoid roadblocks and operational mismanagement, these backend/admin tasks need to be fluid and seamless. This would enable healthcare providers with the ability to provide better patient experiences.
If healthcare providers were to analyze every line of patient-oriented data, they could undo a list of bottlenecks that plague their operations and administrative tasks. However, a study by the World Economic Forum highlighted that 97% of all hospital data goes unused each year.
This data often goes unutilized because of the sheer volume of information generated each day, coupled with other rudimentary issues. Take the case of physician notes. These notes possess the data needed to help forecast models predict follow-ups and patient care outreach. By parsing through treatment markers and scrubbing through historical data, algorithmic models can be deployed to help automate follow-up scheduling, medical advice dispatch, and suggesting secondary treatment pathways.
Sounds simple, right?
Yet, the catch is that most of this data is entered manually in the form of clinical notes, which could pose a problem for computational algorithms. Natural Language Processing provides a speech-to-text platform for doctors to enter clinical notes vocally or utilize Computer Vision (CV) to extract data from hand-written docs. The engine then scrubs through these datasets to deliver relevant insights.
When we extrapolate from the situation and look closely, we see that the automation of operational procedures, intelligent diagnostics, and administrative tasks rely heavily on patient data. Healthcare establishments are now dependent on automation tools, AI-ML SaaS (Software-as-a-Service) vendors, and Virtual Assistants to help scrub, aggregate, and analyze patient-related data for process efficiency. Hence, the entire HIPAA debate becomes a crucial factor across the conversation.
Interoperability of patient data helps increase efficiency across patient care, insurance processing, and administrative tasks. But, HIPAA establishes that any malpractice or mismanagement of patient-related data could expose healthcare providers and solution vendors to libel charges.
The protection of patient data and the prevention of data misuse becomes a key governance pointer for all AI algorithms, Virtual/Digital Agents, and SaaS vendors.
In the case of medical diagnostics, clinical data used to train AI-powered models can be stored on-site, without it being shared directly with establishments. In a now widely accepted technique known as Federated Learning, the algorithmic model is shared with clinics so that data is protected at all times without it jumping plates.
We must also realize that patient data can be exploited for monetary gains if it finds its way into the wrong hands. If a single record exposure costs the US healthcare system upwards of USD 300, the entire industry loses over USD 5 million per day to malware threats.
Machine Learning apps are designed with features to analyze trends that conform to malicious links and requests, which adds a layer of security to all patient data. These applications can also create a validated network between nodes in the network that requires access verification. This feature protects patient data from timed/intercepting hacks.
Admin staff across healthcare institutions also have access to critical patient data. HIPAA compliance ensures that all patient-related data, whether digital or physical, must be safeguarded at all times. When it comes to handling documents, notes or files manually, processes need to be established that create a cloud of accountability and ownership. Hand-offs and patient data transfers across channels (such as HL7) must be conducted with scrutiny and monitored systems. Human error has often been the case if mishaps across the patient data management pipeline.
The best way to combat these errors is by routinely conducting training sessions for all human resources. However, in the event of stray missteps, AI-powered fail-safe mechanisms are employed to verify and validate data transfers and acceptance of requests for EHR retrieval.
A single HIPAA violation against a single data record could cost healthcare providers anywhere between USD 100 – 50,000. When we talk about millions of records, the numbers start to become mind-boggling. In hindsight, it becomes evident that for any technology to be implemented across the patient care/patient management journey, HIPAA compliance is mandatory.
This seal of approval will give healthcare institutions the courage to believe that they are employing technologies/services that protect them from data mismanagement lawsuits or libel charges.
About Pranay Jain
Pranay Jain is the Co-Founder and CEO of Enterprise Bot and is responsible for global business & product development. With his expertise in Finance, NLP, and Artificial Intelligence, Pranay has ensured that the firm is already cashflow positive within its first year of operations with clients and partners like SIX, Generali, and PwC among many other prestigious organizations. He is passionate about FinTech & InsurTech and studied Economics and Finance at Bentley University.
