Data Automation in Healthcare: Everything You Need to Know

The recent report offered by PR Newswire indicates that in 2020 alone, 90% of hospitals used Artificial Intelligence (AI). Importantly, it is presented as a 37% rise from 2019. With global healthcare spending reaching $8.8 trillion, healthcare providers are looking for new ways of tapping into such a massive market. That is why global data automation in the healthcare market is expected to reach $88.9 billion by 2028. With these insights in mind, companies providing healthcare software services to boost automation are in great demand. 

Even before the COVID-19 pandemic, many healthcare providers have looked at automation as the next step in providing high-quality services. Importantly, the phenomenon is delivered through various technologies along with the rise of data automation. In such a context, let’s dive deep into a comprehensive overview of data automation in healthcare. It entails exploring the ins and outs of the phenomenon, finding out the variety of technologies within the scope of intelligent automation, and investigating use cases along with the benefits automation brings. 

When to Know Automation is Needed?

Dealing with massive volumes of patient data and engaging in numerous administrative tasks within a healthcare organization takes a lot of time and depends on various operations. Besides, with the rising number of software solutions presented to the industry, there is an increasing demand for professionals who can multitask and deal with technologies effectively (see Fig.1).

At this point, with organizations becoming more complex and having more tasks performed by healthcare professionals and administrators, it creates an overload and can lead to lagging strategic objectives. Burnout becomes a reality when people deal with multiple tasks, especially repetitive ones. In such a context, procedures that require a high degree of input and energy are the ones that can potentially aid from automation. 

Data automation software is a must if a healthcare organization experiences various repetitive tasks and has a high-priority backdrop within the scope of different operations. Moreover, one can say that almost every healthcare provider on the market experiences problems with repetition and high intensity within various processes. Safe to say, it means that data automation is the approach almost everyone in the healthcare industry can benefit from. Now, it is time to dive deeper into the phenomenon of data automation.

What is Intelligent Data Automation?

In a nutshell, Intelligent Automation is an application of different technologies directed at efficient decision-making while optimizing business operations. The power of Intelligent Automation is delivered through Robotic Process Automation (RPA), Artificial Intelligence (AI), and Business Process Management (BPM). 

However, there are also some auxiliary technologies worth mentioning. Namely, because they bring data automation and management systems to another level. Forbes indicates that Intelligent Automation is crucial for boosting business performance and revolutionizing the entire business world.  

RPA

RPA is a crucial component of data automation. This instrument stems from robotized machines engaging in back-office operations, including form filling and data extraction. 

When coupled with complex algorithms brought by AI, tools delivered by RPA can tackle sophisticated processes that require the analysis of vast volumes of data. In such a case, RPA can also operate with Big Data services to bring seamlessness to many operations within the health ecosystem. IBM suggests that RPA has a bright future if companies can use automation, analytics, and AI successfully. 

AI and Machine Learning (ML)

AI is one of the well-recognized technologies associated with automation. This instrument is based on algorithms mimicking human-type reasoning when analyzing data. In turn, ML is an algorithm that uses outputs from AI and later translates them into predictions. Both tools use digital data automation and allow seeing prospects based on historical data analysis. The AI market is booming, and one can expect an even more remarkable rise, especially in healthcare (see Fig.2).

Deloitte argues that harnessing the power of AI and ML is a direct way to bring automation and analytics into data automation and management. And the number above proves that. As a result, AI and ML services integrated into healthcare open a range of opportunities for running top-notch data management systems.

BPM

The third crucial factor of Intelligent Automation is BPM. It is coined as automating business workflows and processes with automation technologies. The approach streamlines business operations and helps improve the reliability and responsiveness of multiple procedures. In most cases, BPM is used for optimization and improvement of engagement. 

Similarly, BPM plays a vital role in healthcare automation, as it brings the same degree of reliability and responsiveness to various healthcare ecosystems. Here is a list of some most recognized BPM platforms on the market. 

Semantic Software Systems

When turning to auxiliary technologies, the ones often used with RPA, BPM, and AI, one should start with semantic software systems. There is sufficient scholarly evidence pointing out that semantic model-driven frameworks bring the Semantic Web closer to businesses and organizations, thus allowing a higher degree of interaction with clients, customers, and patients.

Simply put, semantic systems make machines and algorithms better understand data. This instrument uses Web Ontology Language (OWL) and Resource Description Framework (RDF) to improve data analytics and make automation more precise. 

Natural Language Processing (NLP)

Better healthcare automation is brought by NLP as well. While part of AI and closely related to linguistics, these tools within Intelligent Automation focus on understanding the human language for better interaction between human agents and machines. 

NLP shows how algorithms should be programmed to analyze natural language data. It allows working with vast amounts of data, which makes it valuable for healthcare. Harvard Business Review coins NLP as the next massive breakthrough in AI. Respectively, the technology can help better understand patient needs and improve the quality of user-provider interaction. 

Computer Vision

While NLP works with natural language data, computer vision is a part of AI allowing machines and algorithms to extract relevant data from different digital sources. They can include images, videos, and other visual-based inputs. After processing such information, computer vision software offers recommendations and aids in decision-making. NVIDIA sees computer vision as having multiple applications in different commercial and noncommercial industries. 

For healthcare, computer vision can be integrated when working with mHealth and telemedicine. You can develop a video calling app and enhance it with computer vision.

Optical Character Recognition (OCR)

OCR is a technology that translates images from type, printed, or handwritten text. The process includes converting the input into machine-encoded text first. Later, an AI-based algorithm can be employed to analyze the data a machine recognizes to offer predictions and some valuable output. Amazon suggests OCR can be important when converting your workflow from paper to digital. 

Within healthcare automation, OCR can help with paper forms, receipts, and images linked to Personal Health Information (PHI). In other words, with OCR, you get more efficient data automation. 

Administrative Applications

Data organization software includes an array of different administrative applications. That correlates to the introduction of AI and is linked to more efficient workflows with few human errors. For instance, administrative applications help deal with Revenue Cycle Management (RCM), reports, and any clinical-related paperwork. These applications help optimize administrative tasks and present up-and-running solutions for data automation and management. 

Rule-Based Expert Systems

When narrowing down the nature of Intelligent Automation to its auxiliary processes, rule-based expert systems is the component to mention. These are rules used to facilitate decision-making. Essentially, professionals take outputs from processed data and set hardcoded principles about aspects like prognosis or treatment. A broader perspective on rule-based expert systems show how properly coded rules can bring automation closer to prognosis, making healthcare professionals’ work easier and more seamless. 

Considering all the above, Intelligent Automation is a phenomenon that includes multiple moving parts. While reliant on AI, RPA, and BPM at its core, there are additional tools that help reveal the true potential of automation. Yet, plunging further into the case, one should determine whether data automation in MedTech is worth considering in the first place. 

Healthcare Data Automation Importance

Speaking from a universal perspective, the interest in data automation in healthcare is visible through the scope of its market size (see Fig. 3).

The graph above shows that the integration of automation into healthcare will continue and grow. From a commercial perspective, it means more healthcare providers and vendors will not be able to avoid automation because it equals a greater competitive advantage. One can expect more healthcare providers to appeal to healthcare software solutions focusing on automation. Yet, the commercial gain is not the only factor indicating the importance of data automation in MedTech. 

Eligibility Checks and Authorization

Eligibility checks and authorization are time-consuming processes requiring plenty of dedicated resources from healthcare providers. Moreover, when handled by human agents, these are areas prone to a high chance of error. Bringing AI and RPA as a part of Intelligent Automation is essential for changing this scenario. It grants more optimization and secured access to insurance information and electronic medical records (EMRs). 

It is hard to overstate the importance of the safe and efficient use of these types of data. Any error can be extremely costly in terms of organizational and patient outcomes.  

Status Checks and Claims

With the rising number of patients and the growing healthcare industry, the difficulty of status administration increases correspondingly. However, with data automation and management systems, professionals can deal with status checks and claims more efficiently. 

It is argued that data automation software submits and processes claims seven times faster than humans. After the claim is processed, the automation can be used to monitor and update statuses at predetermined intervals. Moreover, top software companies offering healthcare solutions integrate alerts linked to claims to indicate when human involvement is needed. 

Coding and Billing Automation

One can expect medical billing to be complex and overbearing. Even when working with a single patient, there is a massive paper or digital trail linked to billing information at some point. Extracting and analyzing data from these procedures can be invaluable. However, it can take a massive chunk of time and resources when done manually or with outdated algorithms. 

In such a case, Intelligent Automation is vital to handle these tasks. AI coupled with RPA, NRP, and OCR can help extract data from any patient records and medical documents. It speeds up the invoicing process and eliminates billing errors. 

Scheduling

Appointment scheduling plays an integral role in any healthcare organization handling patients. Appointment management software rooted in automation makes the entire process more efficient. Intelligent Automation can accurately show how many patients are seeking medical attention and correlate it to the number of physicians available to offer services. From a patient’s perspective, appointment schedules have always been an issue. Yet, with healthcare data automation engineering, patients can easily book, cancel, and confirm appointments. 

Registration

For decades, healthcare professionals have used Electronic Health Records (EHR), a staple for handling registration. However, these systems are not that efficient in handling multiple data automation workflows. When changing or updating EHRs with automation, healthcare providers can access up-to-date patient records in real time. For administrative staff, automation makes data entry more organized and optimized. In this case, if you consider developing or updating your EHR platform, always keep the automation factor in mind. 

Check-Ins

When data entry requires input from human agents, one can expect the chance of human error. That often corrupts the data and adversely impacts the seamlessness of administrative and data management operations. In such a case, handling patient check-ins with precision and accuracy is extremely important in all process cycles. 

Healthcare data automation engineering aided with automation brings the effectiveness of patient check-ins to another level. For instance, professionals can use automation to automatically analyze past visits and check-ins to fill out forms during patients’ future visits. 

Appointment Reminders

MGMA indicates that automated appointment reminders are used by about 88% of healthcare organizations. The number should grow, and the underlying technology can boost customer satisfaction with the care provided. Data automation software helps organizations avoid no-show appointments while helping the patient arrive for a physician visit on time.

Because of unorganized and hectic scheduling, the integration of the no-show factor often brings massive time losses to healthcare providers. At this point, automation creates a win-win situation for providers and patients in appointment scheduling and reminding. 

Informed Consent

Informed consent is vital for delegating multiple procedures within healthcare provision. It can affect administrative data automation workflows and, most importantly, patient outcomes. Bringing healthcare automation to deal with informed consent optimizes the process of data collection. It is essential because physicians and administrators can have tools that illustrate patient opinions, especially those corresponding to their consent. So, when the factor of informed consent is considered, it grants physicians and other healthcare professionals more time to deal with high-priority tasks.  

Referral

As a health system expands, it will experience a growing number of recurring contracts. Managing these requires time and human resources. Importantly, if a provider cannot deal with contracts efficiently, it will experience a financial loss and hectic organization of internal processes. 

Healthcare automation brought by AI presents solutions allowing rapidly scanning contracts to extract vital data. Data automation software can handle multiple contracts at once and compare the information. It results in better risk analysis and more effective supply management. All these factors translate into better patient outcomes and better administration. 

Data Migration 

As mentioned above, EHRs have been used by healthcare organizations for decades. However, the evidence suggests a bit of stagnation in the EHR market (see Fig. 4).

While EHRs still help handle aspects like data migration, the more data these systems encounter, the higher the likelihood of errors and inefficiencies. In such a case, it is vital to find a way to make EHRs more accurate and effective or find substitutes that will be a viable solution to the increasing pressure of data automation flows. Moreover, even a more significant issue arises when speaking of data migration. If you need to move datasets from one system to another, you can face many challenges that EHRs do not resolve. 

With Intelligent Automation, especially RPA, it is possible to consolidate data volumes that enable more effortless movement. In addition, automated healthcare data migration boosts accuracy and saves time. It happens because no records are left behind, and no human-made errors occur. Along with RPA, professionals can use OCR to convert images into readable data. It helps extract data and move it to file systems within EHR or any other system.  

Considering all the key areas that automation can aid with, it is hard to underestimate the importance of the phenomenon brought to the industry. Yet, nothing speaks better of what healthcare automation brings than particular use cases. We have selected several critical examples of practical applications of data automation in healthcare. These speak volumes and prove that healthcare providers cannot afford to ignore tools like AI, RPA, and BPM. 

Data Automation in Healthcare Use Cases

Proper healthcare application development can mean the difference between commercial success and failure. The same is true about healthcare automation, with the stakes being even higher due to patient outcomes being a factor to consider. Without further ado, let’s deconstruct several use cases increasing the worth of data automation healthcare. 

Use Case #1: It Is All About Data

At this point, we see that RPA plays a leading role in delivering the advantages of Intelligent Automation. This assumption can be supported by the evidence of RPA’s growing market size (see Fig. 5).

RPA gained its trust through its ability to handle different types and volumes of data. When it comes to health, data usually comes from multiple sources. These include third-party portals, scheduling systems, insurance vendors, and health record databases. Often, the data automation stream is decentralized and relies on healthcare organizations utilizing massive resources to collect, store, and analyze it.

RPA has proven to play a central role in efficiently processing health data. LexisNexis indicates that adopting Intelligent Automation to handle health data can prevent the industry from losing $2.1 billion due to human error and health data automation mismanagement. At this point, this evidence shows that data management systems fueled by RPA can save billions of dollars. 

Use Case #2: Monitoring Assets

Data automation in MedTech can help track assets effectively, improving patient outcomes and reducing the financial losses linked to incorrect asset management. Each hospital has instruments like medical pumps, ventilators, and defibrillators. 

We mentioned these particular tools because of their utmost importance when the COVID-19 pandemic hit. At that time, incorrect asset management stretched and thinned resources, making tracking assets extremely significant. Staggeringly, it is estimated that nurses spend about 6,000 hours per month looking for lost assets. In a time of urgency, the inability to find equipment can drastically affect patient outcomes.

In addition, Intelligent Automation enables healthcare organizations to use sensors and cloud-based computing to locate and track all assets on the premises easily. With these data data automation and management systems, thousands of hours will be saved, and healthcare professionals will have immediate access to life-saving equipment at a moment’s notice. 

Use Case #3: Next-Level Diagnostics

The advantages of data analytics in healthcare have a direct impact on the effectiveness of diagnostics. Returning to Intelligent Automation, the inability of healthcare providers to collect and analyze vast amounts of patient data results in incorrect predictions and unsupported credible evidence diagnostics. This scholarly source suggests a 0.7% diagnostic analytics error among all admissions. 

Take the COVID-19 admissions in 2021. There were an estimated 32 million admissions in a year. With 0.7% of admissions affected by diagnostics errors, it results in 224,000 patients being affected. Data automation in healthcare can decrease the percentage of diagnostic errors, thus reducing the number of misdiagnosed patients. 

Besides, with access to healthcare now delivered remotely, diagnostics become even more challenging and essential. Tools like RPA can help make mHealth solutions and telehealth applications more accurate. This case shows that automation in healthcare can save lives. 

Use Case #4: Offering Customer Service

Excellent customer service is vital for various commercial industries. However, it has even higher importance in healthcare. About 84% of patients appeal to online reviews to assess physicians. In such a case, customer service plays a leading role. Having a customer service department can be a costly endeavor. Besides, making administrative staff serve as customer support can cost precious time that could have been used to improve patient outcomes. 

Intelligent Automation brought to healthcare offers tools like AI-based voice agents and AI-powered chatbots. These tools can drastically reduce the workload of human agents and almost completely substitute the entire customer support department. They bring the following aspects:

• human-like patient interaction
• 24/7 support
• appointment reminders 
• automated appointment rescheduling and canceling
• valuable information extraction

Data automation and management systems integrated into customer service reduce the number of no-show appointments, boost customer satisfaction, improve the patient experience with round-the-clock support, and improve healthcare employees’ productivity by relieving them from mundane customer service tasks. 

Use Case #5: Compliance with Rules and Regulations

Companies operating with patient data must comply with specific rules and regulations. In such a case, one of the critical standards is HIPAA. It protects PHIs and ePHI and ensures healthcare organizations have procedures helping to handle patient data securely. HIPAA violations result in massive penalties. On average, a company can pay a $60,000 fee for noncompliance. In some cases, the number is as high as $1.9 million.  

With Intelligent Automation, it is possible to boost compliance. AI-powered instruments allow healthcare providers to have systems automatically tracking every authorization and logging. You see who is using patient data automation and what for in real-time. That increases transparency and ensures compliance. As a result, Intelligent Automation helps companies avoid hefty fines associated with noncompliance and allows using HIPAA-compliant software from the get-go. 

Use Case #6: Top-Notch Health Analytics 

Intelligent Automation brings forward advantages of data data automation analytics healthcare. Equipped with data organization software, healthcare providers can run AI-powered analytics to improve different health and operational processes. The National Health Care Anti-Fraud Association estimates that fraud in healthcare costs about $68 billion annually. Thus, automated solutions help detect malicious activity and use analytics to avoid fraud and breaches.

Moreover, using the power of predictive analytics in healthcare using data automation streaming brought by AI and ML, healthcare providers can boost data security, enhance treatment planning, and have more accurate anticipations from drug trials. While data automation in MedTech is not a panacea, it can drastically reduce the chance of fraud being committed, which saves healthcare organizations plenty of finances. 

Benefits of Hospital Data Management for Administrators

At this point, it is apparent that Intelligent Automation is a practical solution for several healthcare organizations’ issues. However, when speaking about some other benefits data automation and management systems bring to administrators, these are the ones to mention:

• Higher task optimization. RPA enables administrators to handle numerous administrative tasks, which takes a massive workload from staff, allowing them to focus on more high-priority tasks.
• Greater staff support. Data automation and management systems boost staff support by automating tools like triage screening and self-diagnosing. It helps the staff to avoid doing mundane tasks, the ones that patients can handle themselves. 
• Seamless patient communication. Chatbots and AI-voice agents use NLP and AI to provide human-like communication to consumers. For instance, with a telemedicine app, administrators can take the bearings of customer service from the staff and let them handle more pressing tasks. 
• Top-grade data security. Intelligent Automation brings forward technologies like blockchain. It enables bank-grade encryption and data security that change how healthcare leaders use and work with patient data to improve care. 
• Intuitive dashboard analytics. Health administrators constantly deal with tasks directed at improving operational efficiency. Dashboards clearly define all the KPIs to guide the decision-making process. Data automation management systems come with highly interactive dashboards offering insights from AI-powered algorithms, which means the ones with high precision. 
• Rapid data transfer. When a healthcare organization decides to improve its operations, it entails a lot of changes. For instance, a healthcare provider wants to shift from paper to digital forms to improve access to patient data. Tools like OCR can be done seamlessly and with a low chance of error. In general, healthcare automation makes data migration and innovation more accessible. 

With the increasing number of healthcare data automation engineering use cases, one should expect more advantages of unraveling the phenomenon. 

Benefits of Healthcare Automation for Patients

The entire healthcare industry works to improve patient outcomes. If your healthcare software creates consumer value, it will succeed. At this point, there are particular patient-based benefits brought by healthcare automation: 

• Fewer medical errors. Medical errors can cost between $4 billion and $20 billion. More importantly, they cost lives. Healthcare automation brings more accurate analytics, which helps better understand patient data and reduce the number of errors made. 
• More accurate diagnoses. Automation can boost clinical decisions while also supporting evidence-based practice. In addition, while diagnosing is time-consuming, automation can speed up the process, thus reducing the chance for patients to experience worsening symptoms due to diagnosis delay. 
• Internet of Things (IoT). The booming IoT market brings new opportunities to improve patient outcomes. Automation coupled with wearable medical devices delivers care outside a hospital. It enables physicians to track patient data and identify diseases at the early stages of their development. 
• Superior patient care. Data automation software enables providers to offer highly personalized care and around-the-clock support. It leads to a better consumer experience and higher quality of care delivery.
• Easier maintenance. With automation, patients can get their test results much faster. With fewer manual machines being used, there are growing opportunities to decrease the reliance on delicate equipment. Besides, patients can use automation software to get their needed information without communicating with physicians.

The advantages above make healthcare automation an invaluable tool for improving care delivery and patient outcomes. However, as with every new technology or instrument, specific challenges exist. Automation technologies are still developing, and some issues are revealed through trial and error. 

Challenges of Healthcare Automation

The golden rule of healthcare dictates that any technology operating with patient data will entail dealing with some challenges. For data automation and management systems, these are the following:

• Patient privacy. Healthcare automation should always come with data security and compliance. Patient privacy is the key factor to consider when adopting automation tools. 
• Resistance to innovation. You need to prepare staff members and healthcare professionals to transition to automation. It will reduce resistance to innovation and will prevent people from feeling like their jobs are in jeopardy.
• High cost. Implementing AI-powered systems can be a costly endeavor. You need to have a budget for that, including expenditures on staff training. 
• Transition delays. Transitioning to healthcare automation takes time. You need to account for the delay to avoid adverse outcomes that the delay period can potentially bring. 

Consider these challenges to avoid being taken off guard. When adopting data automation software, double-check whether you have data security measures in place, you have a sufficient budget, and your staff is aware and ready for a transition.

How to Choose Hospital Data Management?

Now, with all the ins and outs of healthcare automation revealed, it is time to say a few words about ways to choose hospital data management systems. When it comes to automation meeting your needs, these criteria should be considered:

1. Compliance. Check whether the software is compliant with standards like HIPAA, CCPA, GDPR, and HL7.
2. Security. Check whether automation software entails AES 256-bit encryption.
3. Integration. Check whether the software includes third-party integrations.
4. Features. Make a list of features you look for and ask software providers about such features.
5. Learning curve. Check whether learning how to operate a data management system you consider is hard. 
6. Price. Consider your budget estimation and check whether the automation software’s price meets them.

Key Takeaways

Long story short, automation offers various benefits for healthcare. Besides, there are existing use cases suggesting the advantages are not only theoretical but also tangible. With the global healthcare automation market booming further, it is a matter of time when there will be no single healthcare provider able to pass on the option to adopt automation tools. 

The future of healthcare automation is bright. Yet, you should not look at the automation through rose-tinted glasses. There are always factors like security and compliance to consider at all times. Prepare for the transition. Have sufficient funds and make sure your staff knows what is to come. Choose automation software carefully and have the checklist we mentioned before. After all, the final saying about whether automation will bring patient value is always after you.