Impressive progress is happening at the crossroads of healthcare and data science which we explored in part one of this three-part series. An abundance of healthcare data and advanced technologies are opening doors to nearly limitless possibilities for streamlining operations, combatting the challenges of the day, and improving patient outcomes.
In this article, the second in our series probing data science in the healthcare and life sciences industries, we will investigate data science’s expanding role in healthcare and the tools that help providers optimize their data assets and avoid common pitfalls. In addition to my examination of data science training and technologies in the field, Data Society’s Co-Founder, Chief Operating Officer, and General Counsel John Nader will contribute his knowledge of the legal and ethical issues that accompany the expansion of healthcare data applications.
Once a relative laggard in adopting data science technologies, today’s healthcare industry is making up for the lost time. As professionals in the sector increasingly embrace the tools and techniques to leverage their abundant data assets, their data science skills must likewise grow. Healthcare practitioners can only reap the full benefits of data literacy and navigate the perils that data science applications present if they are prepared with the necessary training. Let’s start with a review of some of the data science tools that improve patient care and increase the need for data science training in the healthcare workforce.
Widespread Digitization and the Healthcare Experience
As today’s consumers become more adapted to the convenience and accessibility of digital transactions, their expectations for healthcare encounters have changed. Digitization is helping healthcare organizations create patient touchpoints that more closely resemble their experiences in other areas. Here are some examples:
AI and ML in Inpatient Settings
Machine learning (ML) and artificial intelligence (AI) capabilities have the power to increase operational efficiency and worker responsiveness in inpatient care settings. By automating the repetitive tasks hospital employees have historically managed, these technologies free staff members to focus on patient care and other functions that demand human attention. This workforce support is especially timely given the healthcare industry’s staffing shortage.
In addition, AI tools can help administrators anticipate staffing and equipment needs, driving their resource management decisions. AI-driven predictive analytics and real-time insights into new admissions, likely discharges, and patient treatment requirements help administrators prepare for probable scenarios. Thus informed, hospital administrators can anticipate demands and optimize staff scheduling, reduce patient wait time, limit waste, and plan for the appropriate availability of hospital beds, rooms, and supplies.
Identifying Appropriate Courses of Treatment
With the advent of wearable devices and other new data sources, physicians have access to a broader pool of insights to inform their decisions about patient care. These new healthcare data sources can illuminate additional dimensions of patient health, including social determinants, that provide physicians with a more detailed and personalized view of a patient’s overall wellness and health-related vulnerabilities. Before a patient even presents symptoms, practitioners with the tools to mine the breadth of patient data and apply predictive analytics can foresee conditions, take preventative measures, and even provide precision care. Data science technologies can also aid doctors in making more accurate diagnoses, a critical and highly consequential step in patient care. A 2014 report by BMJ Journals stated that diagnostic errors impact an estimated 12 million US adults yearly. The Society to Improve Diagnosis in Medicine further estimates that misdiagnoses contribute to 40,000 to 80,000 deaths in US hospitals annually.
Contributing to the increased personalization and accuracy of patient care is the rise of data sharing. Facilitated in part by the implementation of interoperability standards that promote uniform terminology and normalization of healthcare data for industry-wide use, the freer exchange of data within and across organizations enables practitioners to connect the disparate dots of a patient’s healthcare background to develop a more thorough understanding of individual histories and needs. However, data sharing presents healthcare organizations with additional responsibilities and advantages.
Avoiding Misinterpretation and Misapplication of Data
The expanding pool of medical data drives dramatic improvements in patient care. Still, the information healthcare data yields can be meaningless at best or harmful in the absence of skilled data cleaning, stewardship, analytics, modeling, and reporting. Here are some examples of data pitfalls that practitioners can avoid given appropriate data science training:
The increasingly smooth flow of data within and across healthcare organizations is without a doubt a mark of progress. However, with this positive trend comes a heightened need for practitioners in the field to navigate the legal and ethical challenges that arise alongside the rise in sharing and usage of patient records.
Healthcare data is proliferating at an explosive rate and is changing hands more readily, thanks partly to the large-scale adoption of Electronic Health Records (EHRs) and Electronic Medical Records (EMRs). In 2019, 89.9% of office-based physicians reported using EHRs or EMRs. This proliferation of healthcare data, the sensitive nature of patient records, the high stakes of medical errors, and the damaging impact of ethical transgressions demands scrupulous attention in the healthcare industry. The legal and ethical considerations associated with the exchange and use of patient data boil down to matters of compliance, privacy, data integrity, and a profound understanding of issues surrounding data science applications.
I would offer a single phrase to encapsulate the critical mechanisms practitioners should implement as a line of defense against legal and ethical lapses—data governance. Many of the same potential pitfalls of data use in healthcare that Dmitri mentioned above apply to legal and ethical risks and the prospect of faulty analysis. And similar solutions can mitigate threats in all of these areas. Skillful data governance demands a thorough knowledge of current local, state, and federal guidelines governing the sharing and usage of patient data and strict adherence to policies and practices that guarantee compliance with these guidelines. Resources dedicated to data quality and security provide safeguards against breaches, biased analyses, and regulatory violations. By investing in these resources, organizations can help insulate themselves from legal and ethical challenges and ensure sound practices in the following vital areas of data usage:
In addition to the established practices that all healthcare organizations should follow, I would also emphasize the need for organizations to adapt readily to current issues surrounding healthcare data usage and any changes in guidelines or legislation. Emerging technologies create dynamic environments as guidelines, and ethical standards are developed in response to new concerns. There are a few legal and ethical issues related to healthcare that I believe will remain fluid as they face further scrutiny in the coming years:
With great progress come great challenges. Data usage and sharing in healthcare are rapidly evolving, and requirements for safeguarding these processes will develop in tandem. Therefore, I stress the importance of ongoing vigilance as healthcare organizations traverse this uncharted terrain. However, given the remarkable benefits of data science applications and the opportunities they present for future advances in the field, it is clear that these resources are well worth the investment in data governance expertise they require.
The incredible advancements that the healthcare industry is making, and will continue to make, through data science technologies offer tremendous hope for the future of patient care. Although the potential challenges, some of which we outlined briefly above, may seem overwhelming, it is encouraging to know that they are surmountable given awareness of broader social implications, thoughtful data governance, and effective data science training across organizations. The technologies propelling healthcare organizations toward new frontiers at accelerating rates, in the hands of a workforce equipped with the skills and knowledge to leverage them responsibly, promise to improve patient outcomes and the experience of all healthcare stakeholders for generations to come.
With more data science applications in healthcare around the globe, it is clear the industry's transformation has already begun. However, achieving the data maturity required to leverage these capabilities effectively does not come without significant infrastructure, culture, and education challenges.
We'll explore how to start a data science movement that impacts: