Gaps in Healthcare
In the last decade, we have witnessed major developments in science and innovation.
We have seen exciting advances that have positively impacted healthcare, halted disease progression, and prolonged patient lives.
In large part, we owe this progress to breakthroughs in biologics and targeting therapies. We are now on the verge of new therapeutic leaps with cell and gene therapies.
New technologies – from genomics to AI and machine learning and predictive analytics – represent provocative opportunities for augmenting our knowledge and driving better clinical decisions.
Nevertheless, we are faced with major gaps and challenges in healthcare and science.
Let’s look at Alzheimer’s disease. It affects more than 50 million people globally and is a debilitating disease. Yet, we still don’t understand the underlying cause of it.
After decades of scientific research, we only have medications that treat the symptoms of the disease. And in the past 10 years, more than 86 Alzheimer’s development projects have been discontinued.
On top of that, for the first time ever, we are going backwards in some important areas.
In developed countries, such as the United States and the United Kingdom, life expectancy at birth is stagnating or declining due to so-called deaths of despair – drug overdose, suicide and alcohol-related conditions – as well as chronic conditions, such as obesity, hypertension and renal failure.
Or consider vaccines.
Vaccines are one of the most effective tools in global health to prevent infectious diseases and epidemics. And yet, issues with access and distrust due to misleading information are leading to outbreaks of preventable infectious diseases, like measles and Ebola.
Studies show that about 30% of all spending on health and healthcare in the U.S. is wasteful – due to things like failures in care delivery, care coordination, overtreatment or low-value care, fraud, and administrative complexity.
Artificial Intelligence is regularly touted as the answer. While it represents a particularly powerful way to generate insights for clinical development, the healthcare ecosystem is still struggling to understand and adopt AI.
We also have gaps in science and research in our approach to data.
Embracing traditional data science alone to make sense of it all isn’t enough.
We need domain expertise and a deep understanding of human health and disease.
So much of what informs, impacts, and enables human health happens outside a traditional healthcare setting - in social care or in the environment.
There is a plethora of de-identified data relevant to health out there – personal health, care delivery, and health system performance.
How do you make sense of it all? How do you interpret the myriad of data to generate consolidated insights?
How do you overcome data bias?
How do you link data derived from sources utilizing different or even conflicting methodologies?
Human science continues to advance, and our knowledge of disease is greater than ever. However, with more knowledge comes new questions.
Cancer is one of the most daunting diseases of all. The more we learn about cancer, the more we understand its complexity.
Gene by gene and pathway by pathway, we have an extraordinary glimpse into the biology of cancer.
Yet scientists can’t explain why cancers continue to proliferate endlessly, and we constantly have to discover new types of cancers, as cancer is no-longer one disease but several hundred.
We need to realize what’s missing, determine the gaps we have and what is needed to move forward.
We need to move from isolated data for the patient to holistic human data.
We need to incorporate a more complete picture of the patient’s life – not just health and care data, but also social determinants and cultural and environmental factors.
We must transition from a technical approach around data science to understanding the broader connection between science, data, and technology that can enable and drive change.
We must move beyond the limited focus on a single condition to dive deeper into the natural history of disease and the underlying biology.
We must transition from looking at disparate and biased data to interpreting carefully curated, interconnected, and linked data-sources.
We are vacillating between hype and hope on the one side and then on the other side, evidence and deliberation.
What we need is Human Data Science.
Human Data Science is an emerging discipline that integrates the study of human science with breakthroughs in data science and technology.
Human Data Science advances our understanding of human health, enabling stakeholders to make better, more insightful decisions so that we can close the gaps in research, patient care, and health system performance.