Leveraging Emerging Technologies to Enhance Data Integrity and Streamline Processes

Integrating emerging technologies can make a huge difference in your organization. New technology can ease the clinical burden on your staff, which can be the difference between your staff facing burnout or not. Access to providers and healthcare can be increased with technology which can be the deciding factor for a patient to choose your organization over others. Emerging technologies can even detect anomalies or mistakes which can help a patient get the care they need faster or give you more accurate data.

Today, we are going to focus on emerging technologies in regard to health information management. We reached out to our talented Healthcare IT Today Community and asked them — how can health information management professionals leverage emerging technologies, such as AI, to enhance data integrity and streamline processes? The following is what they had to share.

Jaimes Blunt, EVP of Infrastructure at emtelligent
Healthcare data volumes continue to grow at an unprecedented rate, and most of healthcare’s critical data remains buried in unstructured text. AI-based technologies that enhance medically trained natural language processing (NLP) not only have the power to mine massive volumes of data at scale but also to understand the deeper context and meaning within each clinical note. By leveraging these technologies, we can unlock a vast reservoir of relevant information that allows health information management teams to make critical healthcare information readily accessible to care providers when and where it’s needed, empowering evidence-based decision-making, streamlining workflows, and improving patient outcomes.

BJ Boyle, Chief Product Officer at PointClickCare
Today, the premise of AI in healthcare is still largely centered on helping staff do their jobs more efficiently – the integrity and free flow of data is a key part of this equation. AI is becoming an essential tool in healthcare and is being leveraged to create new efficiencies – this includes better documentation, streamlined data extraction, and predictive modeling that contribute to enhanced clinical efficiencies and higher-quality care. The AI models of tomorrow will be fueled by a complete picture of each individual patient – this is powered by a responsible infrastructure with an output of safe, and secure interoperable data.

For example, intelligent summarization of patient risk and what has changed in a patient’s health history will help healthcare professionals to better understand when to act and intervene. Empowering care teams and nurturing skill sets with this greater visibility is key to maximizing their time with tools that create better point-in-time intelligence. Bi-directional learning between humans and technology will also help to enhance care quality. As AI advances rapidly, so will the ability to garner a more comprehensive picture of a patient’s health without redundant work.

Jayashree Selvaraj, Strategy Manager – Coding and Clinical Services at AGS Health
HI professionals can leverage emerging technologies, such as AI, to enhance data integrity and streamline processes by automating key data entry, validation, and standardization aspects. AI systems can identify discrepancies and flag potential errors in real-time, significantly reducing the likelihood of inaccuracies in patient records. Machine learning algorithms can also analyze large datasets to detect patterns, assist with predictive analytics, and improve medical coding accuracy. AI-powered revenue cycle management (RCM) systems streamline billing and coding processes, reduce claim denials, and optimize revenue capture.

By automating routine tasks, these technologies allow HI professionals to focus on more strategic activities, improving both efficiency and the overall quality of data management. These tools also help simplify the complicated landscape of payer rules, regulatory policies, and internal guidelines with automated updates, eliminating dependence on human memory to keep up with evolving rules. Finally, AI-powered cybersecurity solutions safeguard sensitive patient health information (PHI). These systems can detect anomalies, identify potential security breaches, and implement adaptive security measures, ensuring compliance with regulatory standards such as HIPAA.

Sandra Johnson, Senior Vice President, Client Services at CliniComp
Health information management (HIM) professionals can leverage AI and advanced voice recognition technologies to enhance clinical documentation by providing real-time suggestions and reducing transcription errors. AI can also automate revenue cycle management, ensuring accurate coding, rectifying billing errors, and optimizing claim submissions, significantly streamlining processes. Additionally, AI-driven workflow automation that is natively integrated into an EHR can more easily handle routine administrative tasks, enabling HIM professionals to focus on more complex responsibilities, thereby optimizing resource allocation and improving operational efficiency.

Arman Samani, President, Therapy Division at Net Health
Emerging technologies like AI show lots of promise for enhancing data integrity, reducing clinical documentation time, streamlining healthcare workflows, and narrowing down clinical pathways to consider. However, a strong foundation is crucial before leveraging such innovations. This baseline ensures that data is accurate and well-organized, which is essential for AI to function effectively. Integrating clinical, financial, and patient engagement workflows into one AI-powered system could significantly reduce fragmentation. Currently, clinicians use separate applications to research patient information and develop treatment plans. By focusing on data quality, healthcare information management professionals can prepare for a future in which AI provides clinicians with integrated access to patient benchmarks, outcomes, and clinical recommendations—all in one place. This will empower healthcare providers to make informed decisions more efficiently and enhance overall patient care and outcomes.

Liz Hyde, Director of Radiation, Imaging, and Special Support Services at Oklahoma Cancer Specialists and Research Institute
Leveraging emerging technologies like AI offers tremendous opportunities to analyze historical data to predict future trends, allowing for more informed decision-making. OCSRI uses the predictive power of Elekta’s oncology informatics platform to assist with budgeting, anticipating case volume, and providing insights into how we can optimize patient care. Additionally, AI functionality supports the standardization and validation of data, ensuring consistency and accuracy across systems. Having real-time access to unified patient data combined with the ability to compare it to historical data helps improve the quality and efficiency of our operations, ultimately leading to better patient outcomes.

Daniel Samarov, VP of Data Science Solutions at Health Catalyst
One of the areas where AI can and will have the biggest impact that we’ll see making its way into health systems in the coming year is streamlining communication workflows. For example, tasks that involve communicating essential patient information, this process can be made considerably more efficient, reliable, and accurate by leveraging AI to summarize and capture relevant information about a patient as it relates to the specific types of outreaches, e.g., referral management, consultation requests, patient handoff, discharge planning, etc.

Today these processes require the provider to spend time searching through, reviewing a patient’s chart, and then reaching out to corresponding providers to ask follow-up questions, which can have several rounds of back and forth. Over the course of a day, this can add up to a meaningful amount of time spent collecting information that could otherwise be pulled together using modern tooling like Generative AI, saving time for providers, and ultimately resulting in better patient care. While I’m bullish that this type of technology will make its way into the mainstream in the near future, it will take time to build provider trust.

Dipak Patel, CEO at GLOBO Language Solutions
Discrepancies in health records, such as incomplete or inaccurate information, are common, particularly when patient data is aggregated from multiple sources, such as during hospital system mergers. This is where artificial intelligence can play a pivotal role in health information management. AI-powered tools like ambient clinical listening can convert provider-patient conversations into clinical notes to add to electronic health records. AI-assisted coding ensures the correct diagnosis and procedure codes are accurately documented. AI can help reconcile health data to prevent the creation of duplicate patient records, improving data integrity, and AI-enhanced scribing allows for more precise medical recording documentation. These AI tools and more are having a tremendous impact on multilingual information capture in the EHR to facilitate higher accuracy levels of interpretation and translation at the point of care.

Shobha Phansalkar, VP, Client Solutions and Innovation, Health Language at Wolters Kluwer Health
HIM professionals can leverage AI in a variety of ways to support a variety of resource-intensive workflows- from enhancing medical coding of patient charts and converting unstructured documents into codified information to creating high-quality data for predictive analytics for Clinical Decision Support or population health analytics. The burden of maintaining high data quality so it can deliver meaningful insights can be incredibly burdensome for organizations without the use of technology. Using AI-powered tools at the point of care enhances the process of creating high-quality data at the source to minimize the rework needed to then “fix” this data and make it usable. AI methods also help create operational efficiencies in cleaning and normalizing large data sets with high accuracy making this data usable for downstream processes. For example, a specialty-tuned large language model (LLM) can help increase the accuracy of domain-specific data so they can ultimately be used for claims and outcomes analysis.

Antonio Marin, CIO at US Med Equip
In today’s healthcare environment, leveraging AI-driven technology is becoming crucial to improving operational efficiency, especially in managing medical equipment. By connecting this new technology to EMR systems hospitals are accustomed to using as part of their clinical workflow, healthcare professionals are more effectively scaling equipment resources based on real-time patient demand. When every moment and every budget dollar counts, this approach is cutting unnecessary costs for underutilized equipment and reducing delays and errors in processing equipment requests. Most importantly, it is saving valuable clinician time, allowing them to focus on patient care rather than managing equipment logistics.

Harshit Shah, CTO at Kyruus Health
The exponential growth of healthcare-related data sources can make using and analyzing data feel like finding a needle in a haystack. With the help of rapidly evolving AI technologies, using these large datasets for meaningful change is possible, especially for health information management. AI-powered tools have the ability to leverage machine learnings to identify patterns in the data, have predictive analytics to suggest trends, flag opportunities to drive cost, and more importantly, help reduce human errors to make informed decisions.

Pratik Maroo, Head of Healthcare and Life Sciences at Zensar
Healthcare organizations and information management professionals must be careful while leveraging emerging technologies such as AI. While AI has enormous benefits in automating cumbersome tasks and in finding new insights, it is equally important to ensure patient privacy and data integrity is not violated.

Public’s trust in the healthcare ecosystem may take irreparable damage if AI models are not fair and unbiased. We have all seen examples of hallucinations in the GenAI use cases. If AI models are deployed in the healthcare arena without adequate protection to manage such wrong behaviors, this may have a catastrophic impact on patient health. Managing compliance with regulations and GxP compliance are other critical areas in which health information management professionals should collaborate.

AI-powered systems can efficiently process large volumes of patient data, including electronic health records (EHRs), medical imaging, and genomic information, to extract valuable real-time insights. When integrated with DMS (Document Management Software) to keep records and forms organized and accessible, this technology reduces the administrative burden on healthcare professionals, ensures data accuracy and consistency, and mitigates the risks that arise from manual errors. Machine learning algorithms can analyze large volumes of data to identify patterns and anomalies that could signal errors. For example, if there’s an abrupt and unexplained change in a patient’s medical record, AI can highlight this for further examination. These algorithms can also compare data from various sources to confirm accuracy, lowering the chances of errors slipping undetected.

There are so many great things to think about here! Huge thank you to Jaimes Blunt, EVP of Infrastructure at emtelligent, BJ Boyle, Chief Product Officer at PointClickCare, Jayashree Selvaraj, Strategy Manager – Coding and Clinical Services at AGSS Health, Sandra Johnson, Senior Vice President, Client Services at CliniComp, Arman Samani, President, Therapy Division at Net Health, Liz Hyde, Director of Radiation, Imaging, and Special Support Services at Oklahoma Cancer Specialists and Research Institute, Daniel Samarov, VP of Data Science Solutions at Health Catalyst, Dipak Patel, CEO at GLOBO Language Solutions, Shobha Phansalkar, VP, Client Solutions and Innovation, Health Language at Wolters Kluwer Health, Antonio Marin, CIO at US Med Equip, Harshit Shah, CTO at Kyruus Health, and Pratik Maroo, Head of Healthcare and Life Sciences at Zensar for taking the time out of your day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you think health information management professionals can leverage emerging technologies, such as AI, to enhance data integrity and streamline processes? Let us know either in the comments down below or over on social media. We’d love to hear from all of you!

Integrating emerging technologies can make a huge difference in your organization. New technology can ease the clinical burden on your staff, which can be the difference between your staff facing burnout or not. Access to providers and healthcare can be increased with technology which can be the deciding factor for a patient to choose your organization over others. Emerging technologies can even detect anomalies or mistakes which can help a patient get the care they need faster or give you more accurate data.

Today, we are going to focus on emerging technologies in regard to health information management. We reached out to our talented Healthcare IT Today Community and asked them — how can health information management professionals leverage emerging technologies, such as AI, to enhance data integrity and streamline processes? The following is what they had to share.

Jaimes Blunt, EVP of Infrastructure at emtelligent
Healthcare data volumes continue to grow at an unprecedented rate, and most of healthcare’s critical data remains buried in unstructured text. AI-based technologies that enhance medically trained natural language processing (NLP) not only have the power to mine massive volumes of data at scale but also to understand the deeper context and meaning within each clinical note. By leveraging these technologies, we can unlock a vast reservoir of relevant information that allows health information management teams to make critical healthcare information readily accessible to care providers when and where it’s needed, empowering evidence-based decision-making, streamlining workflows, and improving patient outcomes.

BJ Boyle, Chief Product Officer at PointClickCare
Today, the premise of AI in healthcare is still largely centered on helping staff do their jobs more efficiently – the integrity and free flow of data is a key part of this equation. AI is becoming an essential tool in healthcare and is being leveraged to create new efficiencies – this includes better documentation, streamlined data extraction, and predictive modeling that contribute to enhanced clinical efficiencies and higher-quality care. The AI models of tomorrow will be fueled by a complete picture of each individual patient – this is powered by a responsible infrastructure with an output of safe, and secure interoperable data.

For example, intelligent summarization of patient risk and what has changed in a patient’s health history will help healthcare professionals to better understand when to act and intervene. Empowering care teams and nurturing skill sets with this greater visibility is key to maximizing their time with tools that create better point-in-time intelligence. Bi-directional learning between humans and technology will also help to enhance care quality. As AI advances rapidly, so will the ability to garner a more comprehensive picture of a patient’s health without redundant work.

Jayashree Selvaraj, Strategy Manager – Coding and Clinical Services at AGS Health
HI professionals can leverage emerging technologies, such as AI, to enhance data integrity and streamline processes by automating key data entry, validation, and standardization aspects. AI systems can identify discrepancies and flag potential errors in real-time, significantly reducing the likelihood of inaccuracies in patient records. Machine learning algorithms can also analyze large datasets to detect patterns, assist with predictive analytics, and improve medical coding accuracy. AI-powered revenue cycle management (RCM) systems streamline billing and coding processes, reduce claim denials, and optimize revenue capture.

By automating routine tasks, these technologies allow HI professionals to focus on more strategic activities, improving both efficiency and the overall quality of data management. These tools also help simplify the complicated landscape of payer rules, regulatory policies, and internal guidelines with automated updates, eliminating dependence on human memory to keep up with evolving rules. Finally, AI-powered cybersecurity solutions safeguard sensitive patient health information (PHI). These systems can detect anomalies, identify potential security breaches, and implement adaptive security measures, ensuring compliance with regulatory standards such as HIPAA.

Sandra Johnson, Senior Vice President, Client Services at CliniComp
Health information management (HIM) professionals can leverage AI and advanced voice recognition technologies to enhance clinical documentation by providing real-time suggestions and reducing transcription errors. AI can also automate revenue cycle management, ensuring accurate coding, rectifying billing errors, and optimizing claim submissions, significantly streamlining processes. Additionally, AI-driven workflow automation that is natively integrated into an EHR can more easily handle routine administrative tasks, enabling HIM professionals to focus on more complex responsibilities, thereby optimizing resource allocation and improving operational efficiency.

Arman Samani, President, Therapy Division at Net Health
Emerging technologies like AI show lots of promise for enhancing data integrity, reducing clinical documentation time, streamlining healthcare workflows, and narrowing down clinical pathways to consider. However, a strong foundation is crucial before leveraging such innovations. This baseline ensures that data is accurate and well-organized, which is essential for AI to function effectively. Integrating clinical, financial, and patient engagement workflows into one AI-powered system could significantly reduce fragmentation. Currently, clinicians use separate applications to research patient information and develop treatment plans. By focusing on data quality, healthcare information management professionals can prepare for a future in which AI provides clinicians with integrated access to patient benchmarks, outcomes, and clinical recommendations—all in one place. This will empower healthcare providers to make informed decisions more efficiently and enhance overall patient care and outcomes.

Liz Hyde, Director of Radiation, Imaging, and Special Support Services at Oklahoma Cancer Specialists and Research Institute
Leveraging emerging technologies like AI offers tremendous opportunities to analyze historical data to predict future trends, allowing for more informed decision-making. OCSRI uses the predictive power of Elekta’s oncology informatics platform to assist with budgeting, anticipating case volume, and providing insights into how we can optimize patient care. Additionally, AI functionality supports the standardization and validation of data, ensuring consistency and accuracy across systems. Having real-time access to unified patient data combined with the ability to compare it to historical data helps improve the quality and efficiency of our operations, ultimately leading to better patient outcomes.

Daniel Samarov, VP of Data Science Solutions at Health Catalyst
One of the areas where AI can and will have the biggest impact that we’ll see making its way into health systems in the coming year is streamlining communication workflows. For example, tasks that involve communicating essential patient information, this process can be made considerably more efficient, reliable, and accurate by leveraging AI to summarize and capture relevant information about a patient as it relates to the specific types of outreaches, e.g., referral management, consultation requests, patient handoff, discharge planning, etc.

Today these processes require the provider to spend time searching through, reviewing a patient’s chart, and then reaching out to corresponding providers to ask follow-up questions, which can have several rounds of back and forth. Over the course of a day, this can add up to a meaningful amount of time spent collecting information that could otherwise be pulled together using modern tooling like Generative AI, saving time for providers, and ultimately resulting in better patient care. While I’m bullish that this type of technology will make its way into the mainstream in the near future, it will take time to build provider trust.

Dipak Patel, CEO at GLOBO Language Solutions
Discrepancies in health records, such as incomplete or inaccurate information, are common, particularly when patient data is aggregated from multiple sources, such as during hospital system mergers. This is where artificial intelligence can play a pivotal role in health information management. AI-powered tools like ambient clinical listening can convert provider-patient conversations into clinical notes to add to electronic health records. AI-assisted coding ensures the correct diagnosis and procedure codes are accurately documented. AI can help reconcile health data to prevent the creation of duplicate patient records, improving data integrity, and AI-enhanced scribing allows for more precise medical recording documentation. These AI tools and more are having a tremendous impact on multilingual information capture in the EHR to facilitate higher accuracy levels of interpretation and translation at the point of care.

Shobha Phansalkar, VP, Client Solutions and Innovation, Health Language at Wolters Kluwer Health
HIM professionals can leverage AI in a variety of ways to support a variety of resource-intensive workflows- from enhancing medical coding of patient charts and converting unstructured documents into codified information to creating high-quality data for predictive analytics for Clinical Decision Support or population health analytics. The burden of maintaining high data quality so it can deliver meaningful insights can be incredibly burdensome for organizations without the use of technology. Using AI-powered tools at the point of care enhances the process of creating high-quality data at the source to minimize the rework needed to then “fix” this data and make it usable. AI methods also help create operational efficiencies in cleaning and normalizing large data sets with high accuracy making this data usable for downstream processes. For example, a specialty-tuned large language model (LLM) can help increase the accuracy of domain-specific data so they can ultimately be used for claims and outcomes analysis.

Antonio Marin, CIO at US Med Equip
In today’s healthcare environment, leveraging AI-driven technology is becoming crucial to improving operational efficiency, especially in managing medical equipment. By connecting this new technology to EMR systems hospitals are accustomed to using as part of their clinical workflow, healthcare professionals are more effectively scaling equipment resources based on real-time patient demand. When every moment and every budget dollar counts, this approach is cutting unnecessary costs for underutilized equipment and reducing delays and errors in processing equipment requests. Most importantly, it is saving valuable clinician time, allowing them to focus on patient care rather than managing equipment logistics.

Harshit Shah, CTO at Kyruus Health
The exponential growth of healthcare-related data sources can make using and analyzing data feel like finding a needle in a haystack. With the help of rapidly evolving AI technologies, using these large datasets for meaningful change is possible, especially for health information management. AI-powered tools have the ability to leverage machine learnings to identify patterns in the data, have predictive analytics to suggest trends, flag opportunities to drive cost, and more importantly, help reduce human errors to make informed decisions.

Pratik Maroo, Head of Healthcare and Life Sciences at Zensar
Healthcare organizations and information management professionals must be careful while leveraging emerging technologies such as AI. While AI has enormous benefits in automating cumbersome tasks and in finding new insights, it is equally important to ensure patient privacy and data integrity is not violated.

Public’s trust in the healthcare ecosystem may take irreparable damage if AI models are not fair and unbiased. We have all seen examples of hallucinations in the GenAI use cases. If AI models are deployed in the healthcare arena without adequate protection to manage such wrong behaviors, this may have a catastrophic impact on patient health. Managing compliance with regulations and GxP compliance are other critical areas in which health information management professionals should collaborate.

AI-powered systems can efficiently process large volumes of patient data, including electronic health records (EHRs), medical imaging, and genomic information, to extract valuable real-time insights. When integrated with DMS (Document Management Software) to keep records and forms organized and accessible, this technology reduces the administrative burden on healthcare professionals, ensures data accuracy and consistency, and mitigates the risks that arise from manual errors. Machine learning algorithms can analyze large volumes of data to identify patterns and anomalies that could signal errors. For example, if there’s an abrupt and unexplained change in a patient’s medical record, AI can highlight this for further examination. These algorithms can also compare data from various sources to confirm accuracy, lowering the chances of errors slipping undetected.

There are so many great things to think about here! Huge thank you to Jaimes Blunt, EVP of Infrastructure at emtelligent, BJ Boyle, Chief Product Officer at PointClickCare, Jayashree Selvaraj, Strategy Manager – Coding and Clinical Services at AGSS Health, Sandra Johnson, Senior Vice President, Client Services at CliniComp, Arman Samani, President, Therapy Division at Net Health, Liz Hyde, Director of Radiation, Imaging, and Special Support Services at Oklahoma Cancer Specialists and Research Institute, Daniel Samarov, VP of Data Science Solutions at Health Catalyst, Dipak Patel, CEO at GLOBO Language Solutions, Shobha Phansalkar, VP, Client Solutions and Innovation, Health Language at Wolters Kluwer Health, Antonio Marin, CIO at US Med Equip, Harshit Shah, CTO at Kyruus Health, and Pratik Maroo, Head of Healthcare and Life Sciences at Zensar for taking the time out of your day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you think health information management professionals can leverage emerging technologies, such as AI, to enhance data integrity and streamline processes? Let us know either in the comments down below or over on social media. We’d love to hear from all of you!