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De-Identified Data key to Advancing Medical Research and Improving Health Outcomes



1. Introduction

Advances in AI/ ML techniques and tools have created very significant opportunities in advancement of medical research. One recent focus of healthcare industry has been in the area of improvements in health outcomes. AI/ ML models allow researcher to not only analyze huge data sets, the models allow researchers to correlate datasets from different hospitals, geographies and genetic background. However, for the researchers to access vast volume of patient data, they must comply with personal data privacy laws. One of the essential approaches to address personal data privacy (PDP) requirements is data de-identification.


Data de-identified has gained significant importance in medical research and for healthcare providers aiming to improve patient care. Sharing data between organizations has the potential to breach the regulations set by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) in the USA and General Data Privacy Regulation (GDPR) in the EU countries. However, through the de-identification process, the sharing of information becomes compliant with HIPAA, GDPR or any other PDP requirements.


The sharing of de-identified data can facilitate the progress of medical researchers in developing tools and treatments. Furthermore, it enables collaborative efforts among large healthcare providers. In summary, de-identification plays a vital role in enhancing the overall patient experience.


2. Healthcare Data De-Identification


By employing the process of de-identification, all direct identifiers are eliminated from patient data, enabling organizations to share it without the risk of violating HIPAA, GDPR and PDP regulations.




Direct identifiers encompass information such as a patient's name, address, and medical record details. While the removal of direct identifiers ensures the confidentiality of a patient's identity, indirect identifiers can still be retained, allowing researchers to analyze data trends. Indirect identifiers may include factors such as gender, race, age, and more. Direct and indirect identifiers as well as medical condition and diagnosis are considered Personal Health Identifiers (PHI).


According to the Department of Health & Human Services (DHS), the de-identification process, which involves the removal of identifiers from health information, effectively mitigates privacy risks for individuals. As a result, it supports the secondary use of data for purposes such as comparative effectiveness studies, policy assessment, life sciences research, and various other endeavors [1].


Protecting patient privacy is of utmost importance in advancing medical research and treatment, making data de-identification a critical component of this process.


3. Using De-Identified Data in Medical Research


Utilizing de-identified data holds great potential for medical research and treatment advancement. Once personal identifying information is removed, this data becomes a valuable resource for enhancing healthcare practices.


A recent study exemplified the application of de-identified data in the development of an artificial intelligence tool capable of predicting the 30-day mortality risks in cancer patients. Considering cancer as a leading cause of death in the United States, this tool enables medical professionals to identify high-risk patients promptly, facilitating early intervention and resolution of reversible complications [2].


Moreover, the tool assists in recognizing patients approaching the end of life (EoL), allowing for timely referrals to palliative and hospice care. By utilizing de-identified data in conjunction with artificial intelligence, the quality of life and symptom management for patients can be significantly improved.




The study authors emphasized the drawbacks of aggressive, life-sustaining EoL care, which can contradict patient preferences and lead to lower quality of life, negative family perceptions of care quality, and regrettable treatment decisions. Early referral to palliative care offers an opportunity to transform cancer care by reducing unnecessary, toxic, and expensive treatments at the end of life, as noted by the study authors [3].


De-identified data can also be used in developing predictive analytics tools. To address healthcare gaps created by the COVID-19 pandemic, UnitedHealthcare developed a predictive analytics tool that used de-identified data to address social determinants to health [4].


At a recent international meeting of medical researchers, Rebecca Madsen, Chief Consumer Officer at UnitedHealthcare, emphasized that approximately 80 percent of an individual's health is influenced by factors beyond their genetics. These factors encompass various aspects of a person's life, including social determinants of health such as socioeconomic status, gender orientation, and other markers that can contribute to disparities in health outcomes [5].


In order to address care gaps, UnitedHealthcare has developed an advocacy system aimed at supporting members facing challenges related to their social environment. Leveraging predictive analytics and a machine learning model, this system can analyze de-identified data from members and identify the requirement for social services.


Subsequently, the data is fed into an agent dashboard utilized by UnitedHealthcare advocates. When a member reaches out for assistance, advocates can leverage the de-identified data to connect the caller with relevant community resources that are either low-cost or free of charge.


The utilization of de-identified data empowers medical professionals to develop tools and solutions that enhance patient care while also driving advancements in research to achieve improved outcomes.


4. Benefits of De-Identified Data


The sharing of data within the healthcare field is instrumental in developing improved tools and treatments to enhance patient care and outcomes. However, the Centers for Disease Control & Prevention (CDC) highlights that patient information must be safeguarded under the HIPAA, GDPR and other PDP laws preventing its disclosure to other entities without the patient's knowledge and consent.


De-identifying data enables providers to share information with other organizations, thereby promoting advancements in medical research and treatment. Moreover, the process of de-identification alleviates some of the concerns regarding potential PDP violations.


Additionally, the utilization of de-identified data fosters collaboration among large data analytics platforms.




By combining the patient data from tens of millions of individuals across thousands of care facilities across the globe, it is possible to establish a substantial de-identified dataset for their analytical efforts and improve health outcomes significantly.


Through the use of de-identified data, providers can effectively share patient information to contribute to medical advancements while upholding patient privacy and complying with PDP regulations.


5. References



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