Healthcare Tech

Ensuring Zero Error and Patient Data Privacy while Adapting to a Changing Model of Care

Digitalization in the healthcare sector leads to improved patient outcomes, thus compelling ISVs and HealthTechs worldwide to prioritize automation for day-to-day operations. The focus is now on data automation as organizations aggressively work toward converting gigantic manual repositories of medical records to streamline them for a simplified exchange of information.

As Dr. Taha Kass-Hout- Vice president of health AI and CMO at a leading cloud service provider, points out, 97 percent of data remains unused simply because it is unstructured and hence incapable of helping researchers and healthcare specialists cross-analyze diseases, arrive at a new hypothesis, or make discoveries.

Minimizing manual intervention and ensuring error-free Ops are on top of the priorities of clinics, labs, and HealthTechs. To boost patient safety and reduce errors, a top healthcare solutions provider in the US has developed an AI-powered tool that streamlines medication refill and renewal requests.

After all, good decision-making rests on the quality of data that is being fed, and automation plays a big role in ensuring high-quality data. Research confirms it eliminates manual errors by 80 – 90 percent and reduces the time spent on rework and review by 70 – 99 percent.
Intelligent automation helps address other concerns like staff burnout, resource utilization, and patient care. It equips HealthTech and Healthcare institutions to curtail costs and enhance efficiencies by automating workflows and enable error-proof processes across the patient pathway, right from primary prevention and screening to diagnosis and medical care.

Here’s a quick lowdown on the manual processes that are on their way to ensuring zero error and patient data privacy.

Intelligent Automation moves the needle in HealthCare 2.0
Advanced technologies like robotic process automation (RPA), cognitive automation (CA), optical character recognition (OCR), natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) are laying the groundwork for zero error and patient data accuracy. Automated ones are now replacing manual and labor-intensive processes to improve patient outcomes.

Some of the most common but critical manual processes that are being automated across HealthCare in the US are as follows:

Sample Collection and Labeling
Lab diagnostics play a critical role in prevention and care, especially since all decisions about diagnosis and treatment rely on them. As per research, up to 70% of mistakes in lab diagnostics can be attributed to pre-analytical errors, and about 24-30 percent of lab errors influence patient care. Needless to say, zero error has become a primary goal of all HealthTechs globally.

Some of the most common scenarios where errors occur include inappropriate test requests, order entry errors, misidentification of patients, sample collection errors, unsuitable containers, sorting and routing errors, labeling errors, and inadequate sample volume. Some of the significant losses incurred by laboratories are often due to human error arising from laborious, time-intensive tasks that cause burnouts and reduce the ability of technicians to perform high-value tasks.

Intelligent automation helps improve pre-analytics efficiency while helping HealthTechs address staff shortages, manage large workloads, and improve morale and work quality. Through automation, HealthTechs are now managing routine tasks like sample collection, labeling, specimen sorting, loading, centrifugation, filling, uncapping, and capping tubes quickly and efficiently.

Data analysis
Data analysis involves processes that capture and analyze data from raw, unstructured data sets to identify patterns and trends for making further improvements. From interpreting diagnostic reports to identifying the correlation between disease and drug treatment options, it includes every aspect of structured and unstructured data. HealthTechs now rely on advanced data analytics to make inferences and aid in clinical diagnostics with personalized treatment plans.

The most common types include:
– Descriptive analytics – looks into historical data that forms the base for future data analysis
– Predictive analytics – involves techniques like data modeling and data mining to forecast possible outcomes and events
– Prescriptive analytics – uses learnings obtained from descriptive and predictive analytics to solve complex challenges and define the right course of action

With the help of technologies like ML, HealthTechs are uncovering new ways to address challenges like staff allocation, distribution and medical logistics, and demand planning.

Diagnostics, Reporting, and Quality Checks
Automation for test procedures and quality control enables HealthTechs to set metrics and use relevant key performance indicators (KPIs) to measure delivery and identify areas for process improvement.

The integration of automation in administrative tasks has improved speed and efficiency, and diagnostic and test centers are utilizing it well for everything from sample handling and test analysis to generating reports, interpreting results, and maintaining records. HealthTechs typically expect a 60 – 80 percent reduction in average time and costs associated with transactional processes.

For instance, a reputed medical technology company specializing in testing equipment and software has automated processes to generate comprehensive reports and minimize errors.

Data Privacy
One of the top electronic health record software providers reported a data breach that led to the stealing of the personal data of more than one million patients, bringing the spotlight on patient data privacy.

Ensuring data privacy and security while adhering to industry best practices has been a challenge for even the biggest HealthTech companies in the US, considering cyber threats and vulnerabilities in the systems.

To begin with, HealthTechs must implement the right security measures, such as encryption and access controls, and train their staff about the importance of following data security best practices. A regular audit must be conducted to identify areas for improvement. While HealthTechs are working on integrating generative AI models into existing analytics and AI roadmaps to generate discharge summaries, manage post-visit notes, and other tasks, they are also exploring ways to secure them with blockchain and legal frameworks to govern their use.

Appointment Scheduling
Consumer needs continue to evolve and challenge HealthTechs to meet their expectations. More than 60 percent of consumers now need the flexibility to schedule or change an appointment online and seek care providers who can deliver continuous remote care 24/7. Those who are satisfied are 28 percent less likely to switch providers and 5 to 6 times more likely to engage with other services offered by the same provider, making the case for patient-centric models compelling.

Established players are reimagining customer engagement across touchpoints and use cases, including appointment scheduling using RPA and other technologies. RPA bots are being deployed to automate patient data collection and appointment scheduling. These bots can even locate openings in the caregiver’s schedule to help patients pick a suitable time. RPA also helps alleviate the burden of manual processes like patient pre-arrival and arrival formalities, such as validating check-ins and pre-authorizing for planned procedures.

Data Entry
Automation begins from the first step, i.e., data entry, and can be further used to log samples, perform tests, validate data, import results, and send them to required locations. Simple processes like data entry can be simplified and streamlined by adding automatic steps to record the right data in great detail.

While there may be conventional EHR (electronic health records) systems in place, they are often incapable of collating data from unstructured data sources like faxes, phone calls, emails, and paper-intensive tasks.

RPA applications can, however, retrieve and store data from multiple sources and convert it into structured information with the help of NLP and OCR technology by automating workflows. A popular telehealth company uses chatbots to respond to patient queries and AI to document patient visits to address administrative burdens and staff shortages.

Managing Documents
An Atlanta-based medical testing laboratory was fined $16,000 as a HIPAA penalty for the delay caused in providing patient records. Proper documentation is critical for overall customer satisfaction. Intelligent automation allows HealthTechs to execute high-value transactional processes like requisitions, purchase orders, invoices, etc., approximately 15 times faster than humans, enabling a high throughput and precision.

Record-keeping can be tedious and necessitates time and resources to update, format, and maintain documents. Medical technicians now rely on OCR and document management systems or DMS to capture and store everything from invoices and contracts to policies and forms in required formats. The fact that DMS can be seamlessly linked with EHRs ensures interoperability and compliance with document management practices. HealthTechs also leverage RPA for billing processes to quarantine incorrect invoices and transfer the bill data into the accounting system.

Automate all Manual Operations
It’s time to break free from repetitive tasks that can be highly detrimental to your organization’s and people’s well-being. A good automation strategy will help increase efficiency and accuracy, enable better decision-making, and lead to cost savings.
By partnering with a competent team of technology experts, HealthTechs can build a tech stack that aligns with their existing systems and frameworks. This will pave the way for achieving the gold standard for healthcare.

By leveraging robust API implementation solutions for EHR/EMR interoperability, HealthTech companies can ensure a fully connected health system that enables patients, caregivers, and healthcare providers to access, exchange, and use electronic health information. With the right RPA solutions, healthcare providers can streamline voluminous & mundane tasks, and provide personalized and timely care through virtual consultations via the telehealth network.

Furthermore, cloud-based lab information management systems & salesforce health cloud solutions enable automated workflows and applications. These help healthcare providers gain better operational efficiency and deliver improved patient care.

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