Electronic Health Records (EHRs) provide a patient data repository that can be analyzed and utilized in unprecedented ways. The potential for these digital records to inform and shape healthcare practices is vast, with benefits ranging from enhanced clinical decision-making to improved patient outcomes. Integrating cloud technology brings unprecedented opportunities in data management and interoperability but presents new challenges in ensuring data privacy and security. Cloud computing represents an innovative solution for the handling and storing of vast amounts of data generated by EHRs.
The adoption of EHRs has seen a significant surge in the last decade, with the Office of the National Coordinator for Health Information Technology (ONC) reporting that as of 2021, 78% of office-based physicians were using an EHR system (Office of the National Coordinator for Health Information Technology, 2021). Further, the ONC noted that hospital adoption of EHRs has skyrocketed from 9.4% in 2008 to 96% in 2021 (Office of the National Coordinator for Health Information Technology, 2021). Additionally, a study by Adler-Milstein and Holmgren (2017) found that hospitals with comprehensive EHR systems have shown a reduction in mortality rates for certain conditions, signifying the potential of EHRs to improve patient care.
Notwithstanding, EHRs provide a wealth of data, encompassing patient demographics, medical history, laboratory results, and medication information. As noted by researchers Ross, Wei, and Ohno-Machado (2016), the integration of these data elements can yield insights that can transform healthcare delivery. They argue, Large-scale EHR data, appropriately standardized, can serve as a formidable tool to understand disease progression, treatment effectiveness, and the impact of care processes on outcomes.
In many cases, EHRs have proved instrumental in improving patient care. A study conducted by the Mayo Clinic (2018) revealed that the use of EHR data in predicting surgical site infections decreased postoperative infections by 74%. On the other hand, EHRs have also faced challenges, particularly in interoperability and data sharing. A report by the American Hospital Association (2017) revealed that only 40% of hospitals had electronic access at the point of care to critical information necessary for informed care decisions from outside providers or sources.
The relationship between EHRs and health provider strategies can be best characterized as symbiotic. The potential for these digital records to inform and shape healthcare practices is vast, with benefits ranging from enhanced clinical decision-making to improved patient outcomes. EHRs can augment clinical decision-making, providing healthcare professionals with comprehensive patient data at the point of care. According to Raghupathi and Raghupathi (2014), EHR systems offer a means to store, retrieve, and process large volumes of data that may allow Electronic copy available at: https://ssrn.com/abstract=4457214 for better-informed clinical decisions. Likewise, a study by Jones et al. (2018) found that clinicians who used EHR data for decision-making reported improved diagnostic accuracy and efficiency. EHRs can also contribute to better operational efficiency. For instance, EHRs automate routine tasks, reduce paperwork, and facilitate communication among healthcare professionals. Andis Robeznieks (2019) revealed that physicians who effectively utilized EHRs reported time savings of up to 45 minutes daily.
EHRs also enhance communication between patients and providers, promoting a more patient-centered approach to care and better patient outcomes. A study by Goldzweig et al. (2015) found that the use of EHRs was associated with improved patient satisfaction, with patients reporting improved access to their health information and better communication with their providers.
EHRs can also facilitate personalized patient care. A study by Wright et al. (2016) found that EHRs allowed physicians to tailor treatment plans more effectively, leading to improved patient outcomes. EHRs allow healthcare providers to monitor a patient’s progress over time, enabling them to assess the efficacy of various interventions. “Electronic health records enable providers to track patient progress more effectively, leading to more informed care decisions and better patient outcomes,” write researchers Krumholz, Terry, and Waldstreicher (2016). As we delve further into this paper, it becomes increasingly clear that the potential for transforming healthcare delivery is immense
Nevertheless, despite the successes noted above, there are still limitations and challenges with current EHRs. One of the major challenges in maximizing the utility of EHRs is the lack of interoperability, defined by the Healthcare Information and Management Systems Society (HIMSS) as “the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner” (HIMSS, 2021). While the 21st Century Cures Act (2016) aimed to enhance health IT interoperability, progress has been slow, with only 30% of hospitals finding their EHR systems’ interoperability above average (Holmgren, Patel, & Adler-Milstein, 2017). The absence of interoperability makes it challenging to consolidate patient data across different platforms, which can significantly impact care coordination, patient safety, and administrative efficiency. Vest and Gamm (2010) asserted the failure of many health information technologies to communicate with each other could contribute to fragmented care and may even create patient safety risks.
Technical issues are also significant barriers to achieving EHR interoperability. They often revolve around inconsistent data standards and formats that hinder seamless data exchange (Payne, et al., 2015). The lack of standardized terminology and protocols can lead to Electronic copy available at: https://ssrn.com/abstract=4457214 misinterpretation of critical information and potential clinical errors. Moreover, the complex architecture of EHR systems often requires extensive customization, which can create further compatibility issues. Bowman (2014) noted that poor EHR technical design and improper implementation could result in significant EHR-related errors, resulting in an expensive legal dispute, highlighting the severity of the challenge posed by technical hurdles.
Adding to these challenges is the quality of EHR data. This has turned into another critical issue for healthcare providers. Incomplete or inaccurate data can compromise patient care and obstruct effective decision-making. Data quality is one of the most significant limitations of EHR systems. This refers to the completeness, consistency, and accuracy of data entered into the system. Incorrect or incomplete data can lead to misinformed clinical decisions and potential patient harm. The National Institute of Standards and Technology (NIST), in their report, “A Robust Health Data Infrastructure,” stated that inadequate data quality is a problem in EHRs, and the potential for misidentification is considerable (NIST, 2014, p. 17).
Interoperability, or the lack thereof, further exacerbates data quality issues. EHR systems frequently struggle to communicate with each other, making it challenging to ensure the accuracy and consistency of patient data across different platforms (Vest & Gamm, 2010). This limitation can hinder effective care coordination and potentially jeopardize patient safety. Additionally, patient privacy and data security remain critical concerns in the use of EHRs. According to data from the U.S. Department of Health and Human Services (2023), healthcare data breaches affected 327 million U.S. patient health records between October 2009 and February 2023, which underscored the critical need for robust data security measures. The exploration of EHRs is undoubtedly akin to navigating an untapped frontier. Their potential, however, is vast, promising to revolutionize healthcare provider strategies and public health decisions.
In March 2023, 63 breaches of 500 or more patient records were reported to the Department of Health and Human Services Office for Civil Rights (OCR), which is a 46.51% increase from February, 6.92% more than the 12-month average, and 40% more breaches than in March 2022.
Data privacy and security are paramount concerns in healthcare. Despite rigorous safeguards, EHR systems can be vulnerable to breaches, risking patient confidentiality and potentially leading to identity theft. A study published by the American Medical Association (AMA), which involved a review of 2,149 healthcare patient record data breaches between 2010 and 2017, found data breaches increased by 70%, accounting for 176.4 million healthcare records, with EHR systems constituting a significant portion of these incidents (McCoy & Perlis, 2018).
The complexity of maintaining privacy in a digital environment is underscored in the case of HIPAA violations, where a patient data breach can result in substantial penalties, potentially threatening the financial stability of healthcare organizations (Miliard, 2015).
EHR systems have been widely criticized for lacking user-friendliness, contributing to provider frustration and burnout. According to a study by Babbott et al. (2014), physicians who use EHRs may be less satisfied with their work and are at higher risk for burnout. They also found that providers spent an extra hour each day dealing with EHR tasks compared to their peers who did not use EHRs. Moreover, design flaws and interface issues may lead to medical errors.
“A patient was treated with trigger point injections of opioids for pain management. The physician intended to order morphine sulphate 15 mg to be administered every eight hours. The electronic health record (EHR) drop-down menu offered 15 mg and 200 mg. The physician mistakenly selected 200 mg. The patient filled the prescription and took one dose with Xanax. The patient developed slurred speech and was taken to the emergency department (ED), resulting in overnight hospitalization and a malpractice claim against the physician for emotional trauma and the costs of the ED and hospital stay” (Ranum, 2019)
Dennis Harms, a lawyer with the law firm of Sandberg Phoenix, stated an EHR could be a blessing or a curse when defending a medical negligence case: In a landmark event, the Congressional Committee for Veterans Affairs conducted hearings after lawmakers were 0 10 20 30 40 50 60 70 80 Apr-22 May-22 Jun-22 Jul-22 Aug-22 Sep-22 Oct-22 Nov-22 Dec-22 Jan-23 Feb-23 Mar-23 U.S. Healthcare Data Breaches Column1 Electronic copy available at: https://ssrn.com/abstract=4457214 informed of the Cerner Corporation’s failure to modernize the Veterans Administrations HER system resulting in 6 cases of catastrophic harm to veterans, including four deaths. Setting the stage for a legal battle for EHR-related patient harm (Jones & Krishan, 2023).
The transformative power of EHRs extends beyond individual patient care, influencing public health decisions at local, regional, and national levels. The comprehensive data EHRs provide can be harnessed to assess disease prevalence, track health trends, and guide policy development. EHRs have the potential to revolutionize public health by providing real-time information on disease prevalence and trends. As Foldy et al. (2014) note, EHRs can provide a timely and comprehensive picture of disease burden in the community, helping public health officials to identify trends and initiate interventions. According to the Centers for Disease Control and Prevention (CDC), EHR-based surveillance increased the speed of influenza detection by 1–2 weeks during the 2017–2018 flu season.
EHR data can be leveraged to predict future health trends and crises. A study by Reis et al. (2016) demonstrated how EHR data could be used to predict a patient’s future risk of receiving a diagnosis of abuse, allowing public health officials to prepare and respond more effectively. The wealth of data contained within EHRs can inform policy decisions, moving public health from a reactive to a proactive stance. As Klompas, Cocoros, Menchaca, and Erani (2017) assert, EHR data can provide a strong evidence base for policy decisions, enabling public health officials to identify needs, allocate resources, and evaluate interventions.
EHRs have been instrumental in informing policy responses to the opioid crisis. A study by Haffajee, Bohnert, and Lagisetty (2018) found that EHR data, including prescription drug monitoring programs (PDMPs), played a crucial role in identifying high-risk patients and informing guidelines for opioid prescribing. The use of EHRs in public health decisions represents a significant shift towards data-driven, proactive health strategies. The potential benefits of AI in healthcare are vast. It can help providers deliver care more efficiently, identify at-risk populations more effectively, and predict future health trends. AI’s ability to sift through large amounts of data can also lead to significant cost savings. Obermeyer & Emanuel (2016) noted that “the use of machine learning in healthcare could generate up to $100 billion annually in the US healthcare system.”
Harnessing the full potential of EHRs requires an integrated approach that addresses the challenges of interoperability, data silos, and data security and allows EHRs to move forward with Artificial intelligence.
EHR interoperability, or the ability of different EHR systems to exchange and use health information, is essential for achieving a truly connected health ecosystem. As Adler-Milstein, DesRoches, and Furukawa (2017) note, the full benefits of EHRs will only be realized when systems can seamlessly exchange data. Improving interoperability can lead to better patient outcomes. According to a report by the Office of the National Coordinator for Health Information Technology (ONC, 2018), hospitals that achieved advanced levels of interoperability had 53% fewer adverse patient safety events.
Fast Healthcare Interoperability Resources (FHIR®) is a healthcare data standard designed to facilitate the interoperability of healthcare information systems. It has been touted as a game-changer in the EHR domain and will support the creation of a healthcare IT ecosystem that can evolve over time (JASON, 2017). FHIR® adopts a modular approach, allowing systems to interact with one another without requiring a complete overhaul of existing system structures (Mandel, Kreda, Mandl, Kohane, & Ramoni, 2016). This aspect of the FHIR® approach is a significant leap toward ensuring EHR systems interoperability.
FHIR® has significant implications for various facets of healthcare, including clinical care, research, and administration. By facilitating seamless data sharing, FHIR® enhances coordinated care delivery and promotes improved patient outcomes. For instance, a study by Bender et al. (2019) demonstrated that implementing FHIR® resulted in a 20% reduction in medication errors due to improved access to patient records.
Evidence suggests that the benefits of FHIR® can outweigh the initial costs and complexities associated with its implementation (Mandel et al., 2016). Moreover, HL7 is continuously enhancing FHIR® standards to mitigate security concerns and support widespread adoption. By enabling EHR systems to communicate effectively, FHIR® paves the way for a more connected and efficient healthcare ecosystem. Though it is not a silver bullet for all EHR challenges, it offers a solid foundation for the future of digital healthcare. As healthcare providers continue to navigate the complexities of EHR interoperability, FHIR® promises to be a vital tool in their arsenal.
With the digitization of healthcare information, data silos have become a significant roadblock in realizing the full potential of Electronic Health Records (EHRs). However, cloud technology promises a way to overcome these barriers, unlocking healthcare data silos while ensuring robust protection of patient data. Cloud computing represents an innovative solution for the handling and storing of vast amounts of data generated by EHRs. According to Alharthi, Alassafi, Walters, and Wills (2017), cloud computing offers significant scalability, efficiency, and Electronic copy available at: https://ssrn.com/abstract=4457214 cost-effectiveness advantages, making it well-suited to the healthcare domain.
The transition from local data storage to cloud-based systems eliminates the issue of data silos, as the cloud provides a centralized platform where data from different sources can be compiled and analyzed (Kuo, 2011). This is particularly important in the context of EHRs, where patient data can often be scattered across multiple platforms, systems, and providers. Breaking down these data silos, cloud computing provides a more holistic and comprehensive view of a patient’s health history. This, in turn, facilitates improved diagnosis, treatment, and overall care delivery while supporting advanced analytics, research initiatives, and data protection.
Given EHRs house sensitive patient information, it is imperative to implement robust data security measures. As Kruse, Smith, Vanderlinden, and Nealand (2017) noted, data breaches and cyberattacks pose significant threats to EHR systems, and healthcare organizations must prioritize security to maintain patient trust. Investing in advanced encryption techniques and regular staff training can help safeguard patient data. A study by Abouelmehdi, Beni-Hssane, and Khaloufi (2018) found that healthcare organizations that implemented advanced encryption and regular staff training can reduce data breaches.
While the integration of cloud technology brings unprecedented opportunities in data management and interoperability, it also presents new challenges in ensuring data privacy and security. That said, advancements in cloud security technology, such as encryption and tokenization, provide robust protection for sensitive patient data (Subashini & Kavitha, 2011). For instance, researchers found that using advanced encryption techniques in cloud computing could protect patient data from common data breaches (Rong, Nguyen, & Jaatun, 2013).
Cloud technology holds significant potential for unlocking healthcare data silos and improving patient data protection. It provides an effective solution for the efficient and secure management of the vast amounts of data generated by EHRs. As the healthcare industry continues to digitize, the integration of cloud technology will undoubtedly play a crucial role in shaping its future.
Artificial Intelligence (AI) is no longer just a theoretical concept but an active force revolutionizing various sectors, especially healthcare. It has immense potential in predicting, diagnosing, and treating diseases more accurately and efficiently. One seminal review by Jiang et al. (2017) affirmed that AI could dramatically improve our ability to capture data and transform it into valuable knowledge to help us make faster and more accurate decisions. This increasing ability of AI to process and analyze vast amounts of data is changing how healthcare providers approach diagnostics and treatment strategies.
The pervasiveness of AI in healthcare has been steadily increasing. According to an Electronic copy available at: https://ssrn.com/abstract=4457214 Accenture analysis, the AI health market was predicted to reach $6.6 billion by 2021, representing a compound annual growth rate of 40% (Accenture, 2017). These statistics indicate the growing importance of AI in healthcare and its potential to transform the sector substantially.
One of the most promising applications of AI in healthcare is its use in conjunction with Electronic Health Records (EHRs). “By capitalizing on the vast amount of data held within EHRs, AI can be used to carry out predictive analysis, improve diagnoses, and better manage diseases” (Rajkomar, Dean, & Kohane, 2019). Analyzing patient data, AI can help healthcare practitioners predict and potentially prevent health issues. For example, AI can analyze data to predict which patients are at risk of developing certain conditions or diseases, allowing for early intervention. The wealth of data contained within EHRs can be used for predictive analytics, a tool that can help identify high-risk patients and intervene early. Research by Amarasingham et al. (2015) revealed that using EHR data in predictive analytics can reduce 30-day readmissions by up to 26%.
AI, in concert with Electronic Health Records (EHRs), has the potential to enhance patient engagement significantly. EHRs constitute a comprehensive source of patient health data, and AI can effectively utilize this data to bolster patient involvement in healthcare processes. This synergy was encapsulated in a study by Bickmore, T. W., Silliman, R. A., Nelson, K., Cheng, D. M., Winter, M., Henault, L., & Paasche-Orlow, M. K. (2013) stating that the integration of AI with EHR data can lead to significant improvements in patient engagement. The impact of AI in bolstering patient engagement is increasingly being demonstrated through measurable outcomes. In a study by Shaik et al. (2022), AI-driven remote patient monitoring led to increased patient engagement rates. This underscores the transformative potential of AI when combined with EHR data.
Improving Patient Engagement: By integrating with patient portals, AI can provide patients with personalized health recommendations and reminders, improving their engagement with their health management. Personalized medicine is a promising healthcare domain, and AI can play a significant role in achieving this goal when combined with EHR data. By leveraging the insights from EHRs, AI algorithms can tailor interventions and treatment plans to each patient’s unique health profile. A study by Topol (2019) supports this notion, stating, “AI, in combination with EHR data, can drive the shift toward personalized medicine.” Patient portals and health apps are vital areas where AI improves patient engagement. With the ability to analyze EHR data, AI can provide personalized health advice and medication reminders and enable patient-provider communication. According to a report by Accenture (2020), 53% of health consumers are open to remote monitoring of ongoing health issues through at-home devices, demonstrating the growing acceptance and utility of such AI-enhanced tools.
In recent years, the utilization of NLP in EHRs has surged. A study by Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., Liu, S., Zeng, Y., Mehrabi, S., Sohn, S., & Liu, H. (2018) revealed that the application of NLP in EHR data analytics has increased by Electronic copy available at: https://ssrn.com/abstract=4457214 approximately 58% from 2010 to 2017, indicating a growing acceptance and application of this technology in healthcare.
EHRs are full of unstructured data in the form of clinical notes, patient narratives, and comments. NLP is the key to mining these textual data to generate valuable insights. As highlighted by Friedman, C., Rindflesch, T. C., & Corn, M. (2020), “NLP can significantly enhance the extraction of meaningful information from the unstructured data present in EHRs.” NLP can inform clinical decision-making by extracting critical data from EHRs, ultimately enhancing patient outcomes. A 2019 study by Shivade, C., Raghavan, P., Fosler-Lussier, E., Embi, P. J., Elhadad, N., Johnson, S. B., & Lai, A. M. stated the utilization of NLP in EHRs can contribute to improved clinical decision-making.
The combination of AI, EHRs, and NLP holds vast potential for improving healthcare delivery and outcomes. With advancements in these technologies, the ability to extract and utilize EHR data will continue to increase, paving the way for a data-driven healthcare future.
Artificial Intelligence (AI) is progressively reshaping clinical research, primarily when used in conjunction with Electronic Health Records (EHRs). AI algorithms can scrutinize thousands of patient records and spot patterns that might elude human analysis, providing valuable predictive insights. EHRs, are rich in structured and unstructured patient data and present an untapped source of insight. AI can effectively mine this data, enhancing the efficiency and scope of clinical research. One review by Murdoch, T. B., & Detsky, A. S. (2013) supports this synergy, stating that the integration of AI and EHR data has the potential to create a powerful tool for clinical research and the discovery of new clinical insights.
The impact of AI on clinical research is not just theoretical but quantitatively significant. Any reduction not only expedites research but also saves costs, underscoring the transformative potential of AI in clinical research. The true power of AI lies in its capacity for predictive analysis. By mining EHR data, AI can predict disease trends, risk factors, and potential therapeutic responses. A study by Beam and Kohane (2018) notes that by using AI to analyze EHR data, we can predict patient outcomes and trends on a previously unimaginable scale. This predictive capacity is a game-changer for clinical research and patient care.
The analysis of EHR data through AI is propelling us toward a new era of personalized medicine. AI algorithms can uncover patterns in patient data to inform treatment plans tailored to individual patient needs, potentially improving patient outcomes and treatment efficiency. Topol (2019) confirms AI is ushering in an era of individualized medicine where treatments are tailored to the specific profile of the patient. The potential for AI and EHRs in clinical research is tremendous. As technologies evolve, we can anticipate faster and more sophisticated analyses Electronic copy available at: https://ssrn.com/abstract=4457214 that will drive further clinical discoveries. However, this future is contingent on overcoming specific challenges and pitfalls.
Despite the undeniable potential of AI and EHRs in clinical research, several challenges must be addressed. Ensuring patient privacy, enhancing interoperability, and addressing inherent bias in AI algorithms are crucial to fully harnessing the potential of this symbiosis in clinical research and beyond.
In the rapidly evolving world of healthcare, AI and EHRs have emerged as integral components in the transition toward more efficient and data-centric healthcare models. As noted by Jha, A. K., DesRoches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris, T. G., & Blumenthal, D. (2009), the integration of AI into EHR systems has the potential to transform healthcare delivery and improve public health outcomes significantly. Using AI allows for meaningful analytics that can inform public health policies. A 2019 report by Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. indicated a nearly 60% increase in the application of AI to EHR data between 2010 and 2018, reflecting the field’s increasing reliance on these sophisticated tools. AI in mining EHR data can reveal public health trends and even predict potential outbreaks, enhancing disease prevention strategies. According to Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G., & Stang, P. E. (2012), the application of AI in EHRs provides a potent tool for public health surveillance, enabling the early identification and mitigation of public health threats.
Population health management, a significant aspect of public health, can be bolstered with the help of AI-empowered EHRs. As pointed out by Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010), by predicting population health trends and risk factors, AI-empowered EHRs can significantly enhance preventative medicine.
The advent of EHRs has ushered in a new era in healthcare, one characterized by data-driven decision-making, improved patient outcomes, and a proactive approach to public health. This transformation, however, is not without its challenges. Interoperability, data security, and training remain significant barriers to the optimal utilization of EHRs. Overcoming these barriers will require concerted efforts from all stakeholders in the healthcare ecosystem.
The potential of EHRs to revolutionize healthcare is vast. As Adler-Milstein, DesRoches, and Furukawa (2017) observe, “EHRs have the potential to transform healthcare, improving the quality, safety, and efficiency of patient care.” The use of EHRs is becoming more widespread. According to the Office of the National Coordinator for Health Information Technology (ONC, 2018), as of 2017, 96% of all non-federal acute care hospitals possessed certified health IT, demonstrating the growing acceptance of this technology.
Despite progress, the journey toward fully realizing EHR potential is far from over. Kruse et al. (2017) caution, “As we navigate the digital transformation of healthcare, we must address the challenges of interoperability, data security, and training to harness the potential of EHRs fully.”
The future of healthcare lies in the optimal utilization of EHRs. As Menachemi and Collum (2011) posit, “The future of healthcare will be shaped by our ability to leverage EHRs to improve patient outcomes, inform public health decisions, and drive healthcare innovation.” Indeed, the dawn of a new era in healthcare is upon us. An era where data inform every healthcare decision, patient outcomes are improved through technology, and public health is proactive rather than reactive. As we usher in this new era, the role of EHRs will be paramount, and our ability to optimize their use will shape the future of healthcare.
References are available in the original publication.
