Harnessing Artificial Intelligence and Natural Language Processing for Data-Driven Healthcare Decisions
April 16, 2024

In recent years, there has been a notable increase in the utilization of real-world data (RWD) to inform decisions and monitor product safety.1 The analysis of RWD produces real-world evidence (RWE), representing a complementary and distinctive perspective for capturing insights from patient experiences in real-world settings (in contrast to the controlled environment of clinical trials).

However, unlocking the potential of RWD requires the processing of large and diverse volumes of data. In this context, artificial intelligence (AI) can be a useful tool to navigate and extract meaning from multiple sources and a valuable resource to inform healthcare decision-making.

The data dilemma: extracting insights from diverse RWD sources

The relevance of RWD in pharma and healthcare cannot be overstated. Understanding the impact of therapies in real-world settings is critical.2 In particular, data from routine clinical practice provide decision-makers a comprehensive understanding of treatment outcomes, patient experiences, and healthcare interventions. However, extracting meaningful insights from RWD presents a significant challenge due to the sheer volume and variety of data sources involved. These include electronic health records, insurance claims, patient-generated data from health monitoring devices, and information posted on social media.3 The diversity of data sources introduces complexities that traditional research methods may struggle to navigate. Issues such as standardization, variations in data quality, and the lack of interoperability among different data types complicate the process further.

Enhancing RWD use with artificial intelligence and natural language processing

AI’s ability to efficiently process large datasets enables the extraction of patterns and correlations that might otherwise remain hidden. Natural Language Processing (NLP) is a subset of the AI field that enables machines to understand, interpret, and generate human-like language. A prominent example of NLP is ChatGPT, a generative language model that utilizes deep learning algorithms to analyze and comprehend natural language input. This capability allows ChatGPT to generate human-like responses based on the input received.

In healthcare, NLP can be used to extract important information from clinical notes and reports, such as patient symptoms, diagnoses, and treatment plans. This extracted information is instrumental in enhancing clinical decision-making processes, leading to more informed and personalized patient care. Furthermore, NLP can be employed to analyze patient electronic health records, extracting valuable insights such as medication adherence patterns or adverse events documented in clinical notes. By identifying and classifying patient-reported symptoms from unstructured text data, NLP algorithms enable the assessment of treatment effectiveness in real-world settings.

Challenges in AI and NLP for RWE

Despite the enormous benefits, there are important challenges in incorporating AI and NLP in RWE. While these technologies offer transformative potential in unlocking richer insights from RWD, challenges such as data privacy concerns, reliability, and variability in data sources pose significant hurdles.4 Data privacy remains a top concern, particularly when analyzing unstructured text with NLP, necessitating robust strategies to protect sensitive information. The implementation of measures, such as anonymization techniques, data access controls, and encryption methods can help to mitigate concerns with data privacy. While the reliability of AI algorithms in generating accurate and unbiased results is an emerging challenge, the continuous validation and calibration of AI/NLP algorithms, using diverse datasets, may help to enhance trust among stakeholders. Furthermore, the diversity of data sources, ranging from electronic health records to social media data, adds complexity to standardization and normalization efforts. To address this issue, data harmonization (e.g., ontology mapping) methods should be continuously implemented to insure reconciliation between disparate data sources.

What next?

The continual integration of these technologies holds immense promise for shaping evidence-based healthcare decisions, ultimately paving the way for a more responsive, patient-centered, and data-driven healthcare landscape.

Medlior can help unlock the potential value of AI and NLP in your RWE projects. With access to comprehensive RWD and a dedicated team of highly qualified specialists, we can support you in using the most advanced methods to incorporate NLP into your data analysis and decision-making, with a robust approach to mitigating the potential challenges.

Contact us at to learn more about our services and how we can leverage RWD to drive meaningful insights and improvements in healthcare outcomes.

References

  1. Jansen MS, Dekkers OM, le Cessie S, et al. Real-World Evidence to Inform Regulatory Decision Making: A Scoping Review. Clin Pharmacol Ther. Feb 23 2024;doi:10.1002/cpt.3218
  2. Blonde L, Khunti K, Harris SB, Meizinger C, Skolnik NS. Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician. Adv Ther. Nov 2018;35(11):1763-1774. doi:10.1007/s12325-018-0805-y
  3. Liu F, Panagiotakos D. Real-world data: a brief review of the methods, applications, challenges and opportunities. BMC Med Res Methodol. Nov 5 2022;22(1):287. doi:10.1186/s12874-022-01768-6
  4. Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med. May 2023;158:106848. doi:10.1016/j.compbiomed.2023.106848