The healthcare industry continually develops innovative ways to collect, combine, and mine health data. However, a patient’s needs can quite easily be missed while we drown in a sea of information. Better information design and user experience are key to developing systems that facilitate informed decision-making in healthcare.
Lifestyle and environment play a critical role in determining an individual’s state of health and wellbeing. In the U.S., poor diets, sedentary habits, work stress, and lack of sleep contribute to epidemics such as obesity, diabetes, and cardiovascular disease. A rise in exposure to environmental pollution subjects people to a higher risk for developing diseases and disorders such as asthma and cancer.
Studying these issues has been an uphill battle for the health researchers. However, metrics from sensors and devices, social media, and other electronic systems provide new data streams, which have the potential to help understand human health better.
Conventional forms of health data depends on two primary sources – clinical assessments and surveys. Clinical assessment data is collected by trained observers using proven protocols and instruments. This process is highly controlled and can be time consuming and expensive. Survey methods, on the other hand, provide a chance to produce large sample sizes, but they are likely to have self-reporting and retrospective biases.
Relatively inexpensive fitness sensors are commonly available in a range of wearable and personal technology such as watches and cell phones. Data from these devices is collected during the patient's day-to-day activities, thus providing a higher level of ecological validity. This data can bring to light various aspects of health and lifestyle that were previously difficult, if not impossible, to measure. Yet, there is still a lack of interoperability; the ability for systems and apps to exchange information. Even within organizations, the ability to gain insights from multidimensional complex data sources is exceedingly limited. Standards such as Fast Interoperability Resources (FHIR) and increased awareness about the importance of UX in healthcare are a cause of increased data-sharing across organizations and enabling deeper insights from a breadth of data.
There is a widespread call for “precision medicine” – the customization of care to individual patients. While the term is frequently used in reference to genetic profiling (e.g., for cancer risk, pharmacogenetics, etc.), individual lifestyle and environment are also key factors in precision medicine.
"Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person" - National Institutes of Health (NIH).
In simple terms, precision medicine works in contrast to the “one-size-fits-all” approach of traditional care. To elaborate further, the National Research Council (NRC) explains:
“Precision Medicine refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease... Although the term ‘Personalized Medicine’ is also used to convey this meaning, that term is sometimes misinterpreted as implying that unique treatments can be designed for each individual.”
A recent announcement of massive funding toward precision medicine by President Obama administration is prompting a bearish sentiment among medical businesses.
President Obama describes precision medicine as:
Mr. Obama announced a $215 million effort to gather genetic data for 1 million Americans. The plan aims to find the genetic causes of disease and discover new drugs to target those causes.
Interfaces and data visualizations for precision medicine must be intelligible, meaningful, and actionable for the people who depend on these medicines in their everyday work. Skilled user researchers are required to understand the nuances of peoples’ behavior, and expert designers are needed to turn that understanding into tangible interfaces.
The NIH's objectives for the Precision Medicine Initiative include the blending of medical records and genomic data with lifestyle information. For example, following up on calorie consumption and environmental exposures through portable health devices to investigate our health. Unlike data collected specifically for research, personal health data is often sourced from the Internet. In a personalized context, an activity tracker might encourage a person to get more exercise, insomnia can be tracked by a sleep monitor, a smart fridge might remind its owner to buy healthy food, or social media streams can mine behavioral data.
We can develop powerful ways to comprehend correlations in exercise, sleep, diet, and mood amongst others, on combining and examining alongside comparable data from thousands, or perhaps millions of other people. This may lead to further investigation into the root causes of health problems. Accessing, processing, and deciphering this data in an important and responsible fashion is, nonetheless, not simple. The Health Data Exploration project, formed in 2013, was aimed at identifying challenges and catalyzing the utilization of personal health data for public research.
In addition to the environmental factors discussed previously, genomics also plays a major part in precision medicine. With the decreasing cost of sequencing DNA and next-generation sequencing technologies, we can look at individual molecular aberrations to understand how a patient's system has gone awry and then prescribe therapies that target that mutation. This approach has already proven very successful in certain cancers.
Interests of three crucial set of stakeholders must be aligned to use personal health data for research. First, the people who generate private health data must be willing to share it with researchers. Second, researchers must be willing to employ the new kind of data. Third, the organizations and companies that gather and manage the data from these devices and applications must be willing to offer access to researchers.
In mid-2013, University of California, Irvine conducted surveys and meetings to better understand the viewpoints and responsibilities of each of these three groups of stakeholders. A total of 465 individuals and 134 researchers were surveyed. They also conducted interviews with 11 of the individuals, 9 researchers, and 15 key informants from companies working in this area. The researchers gained confidence that while there are various difficulties that should be tended to, there is huge opportunity to improve human wellbeing and health through sharing of data.
In spite of the fact that people are willing to share their data, they are constantly worried about the utilization process of the data. A large portion of the participants said they didn’t want their data to be used for marketing or revenue-driven activities of organizations.
Privacy: A noteworthy issue
The privacy issue was a compound set of worries among the participants. Their concerns were about what data would be shared, how the data would be utilized, who might have access to the data and to what extent, what rules and legal securities were set up around the data, and their capacity to know each of these elements and control the destiny of their data.
Inadequate ethical norms and regulatory procedures
Because of inadequate methods, researchers did not have a good comprehension of the risks associated with displaying personal health data. Many standard processes for informed consent and safety scanning become exceedingly complicated if data is shared openly.
Companies’ desire to protect customers
User trust is of utmost importance to any commercial organization. Companies must avoid any breach of privacy or use of personal data that makes customers lose faith in them.
Precision medicine also has its share of difficulties. Joel Dudley, Director of Biomedical Informatics at Mount Sinai School of Medicine, believes that some of the same technological capabilities that are driving precision medicine forward are also the source of its biggest challenges.
“This is all really being enabled by an explosion of data and it’s concerning to me that the things that are making this possible are also presenting real problems in terms of information overload for physicians and patients,”
says Dudley. He believes not much effort has been expended to figure out how to put the mountains of data to practical use.
“I think a lot of the effort and funding around precision medicine has been spent on collecting more and more data and building algorithms to put data together and analyze it.”
User experience research and design play a huge part in overcoming information overload, however, there is a marked lack of funding for designing usable systems. He continues,
“To my mind, it shows a real lack of appreciation of the role of design in this area.”
Eric Dishman, director of the Precision Medicine Initiative (PMI) Cohort Program, is the expert tasked with spearheading an effort to create a cohort of 1 million Americans.
Dishman was asked about handling all the data. He said:
“If you sequenced the genes of the 1.65 million Americans who are going to be diagnosed with cancer this year just once, and then you add clinical and imaging data from their electronic health records, you'd have 4 exabytes of data. That is the equivalent of all of the data that's in the Library of Congress. Five exabytes represents the digitization of all human words every spoken. And this is just 1.65 million Americans with cancer. Precision medicine is going to be about Alzheimer's, diabetes, Parkinson's and the prevention of disease, everything you can imagine.”
All of the challenges of precision medicine aren't quite yet solved. If we look at childhood cancer as an example, there is a functional inability to gather sufficient data on the mutations that contribute to pediatric cancer. Childhood cancers are far less common than adult cancers. It is estimated that 10,380 children in the U.S. under the age of 15 will receive a cancer diagnosis this year. As a comparison, there will be more than 246,000 new cases of breast cancer in women diagnosed in the U.S. this year.
The rarity of childhood cancer makes it harder for scientists to gather a sufficient amount of genetic data. Additionally, there are notably fewer genetic mutations in childhood cancer than adult cancers, mainly because children are exposed to fewer environmental and lifestyle factors that can alter their DNA. Another barrier to precision medicine for children’s cancer is that there exist few targeted drugs, whether experimental or approved, that are available for children. Most drug makers focus on developing targeted remedies for adults, and current guidelines on dosages are often unsuitable for children.
However, targeted therapies for pediatric cancer and many other diseases are slowly reaching the market with the advent of relevant scientific tools and technology.
Precision medicine is powered by emerging forms of partnership and new feedback loops between the processing power of machines and the people interpreting the output from various tools and interfaces. There is a tremendous need for user-centered design in healthcare. Many of the challenges facing precision medicine have user experience components. The quality of interfaces and data visualization techniques will have a significant impact on our ability to understand the volumes of health data we will generate in the coming years. This should be a wakeup call for UX and healthcare professionals to be proponents of change and drivers of innovation in these exciting times.