Goals and challenges in life sciences, pharmacology, and healthcare

  • 4 minutes

Healthcare is a large and diverse sector. It encompasses a wide range of services, from medical research, pharmaceutical manufacturing, preventative care, to treatment. It’s provided by research institutions, hospitals, nursing homes, outpatient care centers, home healthcare, and other medical service providers. This sector employs a skilled workforce of trained professionals including doctors, nurses, technicians, and administrative staff working together to deliver quality care to patients.

Despite its size and complexity, there are some shared goals and challenges that are crucial for the healthcare industry.

Goals

All healthcare professionals share these objectives. As all healthcare practices, AI use cases must adhere to them.

  • Improve and maintain the health and wellbeing of patients: The principal and fundamental objective of healthcare remains simple: to provide safe, effective, and high-quality care to everyone.
  • Cultivate a supportive, diverse, and empowered workforce: AI has the potential to enable a work environment that prioritizes the well-being, development, and satisfaction of healthcare workers while facilitating the delivery of excellent patient care.
  • Move care closer to homes: There’s a growing interest in making healthcare more accessible. Taking healthcare out of hospitals and into households is a strategy to reach more people.
  • Safeguard patient data and maintain regulatory compliance: Healthcare data is sensitive. It includes personal data that must be kept confidential at all costs. Governments require healthcare data management to follow strict protection measures, so privacy and security concerns should guide data policies to achieve compliance.

Photograph showing healthcare worker holding a tablet.

Challenges

Healthcare faces some specific challenges when implementing AI that you should consider during your AI journey.

  • Data management and governance: To be useful, data needs to be consistent. Cleaning data for healthcare AI projects can be daunting. Besides, all data operations must comply with strict regulations. Risk tolerance in data governance is lower in healthcare than in other sectors, because of the importance of patient safety, rigorous regulations, ethical obligations, the potential consequences of errors, and the imperative to maintain trust and reputation in the eyes of both patients and the public.
  • Lack of data/AI infrastructure customized for healthcare: Too often, data scientists working on healthcare scenarios struggle to find basic tools for their work. For example, it’s hard to find consistency between healthcare AI models. There’s also a lack of AI data ingestion layers customized for the industry. Finally, there aren’t many available models that can be compliant and that are already trained on healthcare data.
  • Model validation: AI systems require industry experts to validate the model’s results. This workflow requires a medical involvement that can sometimes become difficult. Moreover, AI models need to avoid overfitting, that is, teaching the algorithm too specific examples that can’t be generalized. Usual AI workflows avoid overfitting models, but this task is hard in healthcare. Each patient is unique and so are many medical situations.

Tip

Consider other goals and challenges that are specific to your organization. Photograph showing people working and talking around a table.

Next, let’s consider the opportunities in life sciences and pharmaceutical healthcare scenarios.