Informatics Trends to watch in 2020

by June Kaminski, RN MSN PhD(c) Editor in Chief

CJNI was initiated by June Kaminski in 2006 when she was President-Elect of CNIA. She is currently Curriculum Coordinator of a BSN Advanced Entry nursing program at Kwantlen Polytechnic University; Past President of the Canadian Nurses for Health and the Environment and Editor in Chief of the Online Journal of Nursing Informatics In 2012, June was honored to receive the CASN and Canada Health Infoway’s inaugural Nursing Faculty E-Health Award 2012 in Ottawa Canada. She also won the Distinguished Teaching Award from Kwantlen Polytechnic University in 2016. She offers the Nursing Informatics Learning Centre for nurses.

EDITORIAL

Technology and informatics-related processes and activities continue to evolve with each passing year. As we finish 2019 and move into 2020, a few trends in this evolution come to mind – ones that could serve healthcare and nursing well. Here are my predicted top trends for 2020.

Speech Recognition Software

Several experts including Dr. Eric Topol have advocated for speech-recognition software to address the burn-out and time consuming need to document through typing into electronic health systems. Although voice-recognition software has been available for decades, the precision that is needed to be useful to healthcare has rarely been realized until recently. Even now, available software requires more sophistication to be seamless and adaptable to health provider needs. Dr. Topol expanded on why voice recognition software could be a real boon for healthcare communication and documentation through “…keyboard liberation or using natural language processing of speech to synthesize notes and eliminate the ultimate source of distraction and dislike in medical encounters” (O’Connor, 2019, p.1). This is a trend to watch for in 2020, one that could make nurses’ work life much easier and less time-consuming.

View the Video: The Benefits of Speech Recognition – G2 Speech (1:26 minutes)

As well, speech-recognition software can act as an assistant to people in general. For example, Amazon Alexa and Leela can interface with clients to help them to set appointments, receive results, and record readings. This virtual assistant approach can also be used within healthcare facilities to enhance access to information and support. This is another trend to look out for this year. View this brief video to see examples of these systems in action within people’s homes:

Voice technology for the healthcare industry (0.32 minutes)

Personalized Healthcare

Machine learning, artificial intelligence, wearable and portable biosensors will contribute to the rise of true personalized medicine and healthcare. This trend includes personalized or precision medicine, defined as “…the tailoring of medical treatment to the individual characteristics of each patient. The approach relies on scientific breakthroughs in our understanding of how a person’s unique molecular and genetic profile makes them susceptible to certain diseases. This same research is increasing our ability to predict which medical treatments will be safe and effective for each patient, and which ones will not be” (Personalized Medicine Coalition, n.d., p. 1).

Personalized healthcare also has a more expansive meaning, that incorporates client preferences, choices, and input to truly tailor their own healthcare. “An array of new technology advancements, including 3-D printing, robotics, nanotechnology, genetic coding, and therapeutic options can permit more personalized and accessible patient care. Many devices and equipment are getting smaller and more portable, and treatments will likely become more targeted—all of which can make future health care more mobile and precise. This, in turn, should increase staff and process efficacy and improve patient outcomes, as clinicians will be able to quickly find the best treatment option rather than try multiple interventions. Personalization of medications, for instance, will be based on a patient’s genetic profile and the use of precision medicine, whereas designs for 3-D-printed prostheses will be based largely on a patient’s specific anatomy” (Deloitte Development, 2017, p. 6).

Many people use health apps that they can download from the Apple or Android Stores to monitor their own health particulars, track health behaviors or data progressions such as weight loss, or fertility related data. People also often use wearable devices such as fitness trackers to monitor and track their physical activity which may include GPS related data such as location, and routes used for running and walking. Other apps may be used to monitor more physiological data such as pacemaker activity, heart rate, blood pressure, and so on. All in all, apps must meet standards to be recommended to clients. Health professionals can suggest which apps are the best choices for keeping their data secure, affording the best experience for the client through expert design, and API connected so that data can be shared with their health providers if they so choose. 

Mobile apps used within practice need stringent measures built in to ensure personal health information (PHI) protection using APIs, encryption, and data capture solutions. Using mobile and wearable apps can significantly boost communication and understanding between health professionals and patients. When apps are designed according to national standards, they can be very efficient modes for sharing data on a regular basis that can support goals, tracking to gauge progress, and keep an eye on conditions that require monitoring which all contribute to personalized healthcare.

View this brief video to see examples of these systems in action:

Dr Eric Topol: Preparing the healthcare workforce to deliver the digital future (3:57 minutes)

Predictive, Prescriptive and Cognitive Analytics

As technology continues to evolve, exciting new abilities are emerging which show great potential in supporting personalized, value-based care. Technologies such as artificial intelligence (AI), predictive analytics and cognitive computing all show great promise in helping health professionals make valuable predictions of trends, diagnoses and outcomes. “AI is the field of study that attempts to replicate human abilities, but without human limitations of time, energy and power. By using advanced algorithms, data processing capabilities and IT systems can produce data driven predictions within seconds – with little to no human intervention. Predictive analytics backed by real time and historic data processing can identify risky medical conditions ahead of time.

Predictive analytics uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. In medicine, predictions can range from responses to medications, to hospital readmission rates. Examples include predicting infections from methods of suturing, determining the likelihood of disease, helping a physician with a diagnosis, and even calculating future wellness” (Patil, 2019, p.1).

Innovators in healthcare technology are working on ways to seamlessly incorporate predictive analytics and AI into electronic health records (EHR) using deep and machine learning coupled with natural language processing techniques to move more towards personalized healthcare. Predicting outcomes from historical data is nothing new in healthcare – health professionals have used it for years. For instance if people engage in certain behaviors such as smoking, eating a high-fat, high-sugar diet, living sedentary lives, and so on, health care professionals can predict likely outcomes. However with predictive analytics, health professionals can tie together all sorts of protected health information (PHI) or big data to personalize predictions beyond common generalizations such as smoking leads to lung cancer or chronic lung conditions in many people. But it does not stop there: predictive analytics is just the beginning of imminent powerful analytics in healthcare. Analytic types currently include descriptive, diagnostic, predictive, prescriptive, and cognitive analytics (Figure 1).

Figure 1: Descriptive, diagnostic, predictive, prescriptive, and cognitive analytics.

Predictive analytics is only one type of analysis that can be used in healthcare. An essential dichotomy of analysis methods are now within reach – each level provides a unique perspective of health data and degree of sophistication. Descriptive and diagnostic analysis have been used in healthcare for decades, but predictive is just emerging, and prescriptive and cognitive analysis are still very rare.

Prescriptive analytics uses optimized algorithms, modeling, rules and decision techniques to build on predictive results and propose actions to be taken. “Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made, it tries to provide how to do the actions and what are the consequences of these actions to derive a better decision. Prescriptive Analytics can be used anytime you need to advise users on what action to take” (Sami, 2017, p. 1).

“Cognitive analytics combines a number of intelligent technologies like artificial intelligence, machine-learning algorithms, deep learning etc. to apply human brain-like intelligence to perform certain tasks. Basically, this type of analytics is inspired by how the human brain processes information, draws conclusions and codifies instincts and experience into learning such as understanding not only the words in a text but the full context of what is being written or spoken. All these intelligent technologies make a cognitive application smarter and more effective over time by learning from its interactions with data and with humans” (WeirdGeek, 2018, p. 1). 

Together, predictive, prescriptive, and cognitive analytics can transform healthcare in astounding ways. 

Although predictive analytics offers amazing insights that can support patient outcomes, personalizing healthcare also requires prescriptive and cognitive analytic capabilities coupled with stringent security features. Systems like IBM Watson can help health professionals support their clients in personalized ways that customize their treatments, client education, and healthcare decisions.

Watch the following video, for greater understanding of how predictive, prescriptive and cognitive analysis and AI can be applied to personalize healthcare for individual clients.  

How It Works: IBM Watson Health (4:03 minutes)

3D Printing

3D printing is one of the most amazing and practical technologies that is rising in healthcare. “3D printing has come a long way since its debut, especially in its uses in the healthcare industry. The technology offers faster prototypes, creating everything from personalized prosthetics to “poly-pills” at a fraction of the cost. The customizable aspect of 3D printing is revolutionizing organ transplants and tissue repair, and it’s even able to produce realistic skin for burn victims” (Ghosh, 2019, p. 1).

This astonishing breakthrough presents mega-opportunities to health care since these printers can be tailored to use anything from plastics to stem cells as their medium. Artificial bones can be constructed that are accepted by the body and work well after surgical reconstruction. Prosthetics can be customized more accurately for amputees, and poly-pills can be created that combine several drugs at once for people with multiple illnesses.

View the following videos to get an idea of how 3D Printing can revolutionize healthcare.

The Ultimate List of What We Can 3D Print in Healthcare – The Medical Futurist (2:58 minutes)

3D printing for biomedical applications (2:54 minutes)

Although there are many other technological breakthroughs coming down the pipeline of innovation, these trends of Speech Recognition Software, Personalized Healthcare, Predictive, Prescriptive and Cognitive Analytics, and 3D Printing are far-reaching and utilize many of these other innovations in their processes. Keep your eyes open to see how these predictions play out over the next year, and how they affect or could enhance your professional practice.

References

Conduent. (2019, February 6). Voice technology for the healthcare industry. Video File. Retrieved from https://youtu.be/HiGZHuiUO5c

Deloitte Development. (2017). The hospital of the future: How digital technologies can change hospitals globally.Retrieved from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/us-lshc-hospital-of-the-future.pdf

G2 Speech.  (2019, June 6). The Benefits of Speech Recognition – G2 Speech. Video File. Retrieved from https://youtu.be/GdA6g1gHOcc

Ghosh, I. (2019). 5 Ways Technology is Transforming the Healthcare Industry. Visual Capitalist, February 27.Retrieved from https://www.visualcapitalist.com/5-ways-technology-healthcare-industry/

Health Education England. (2018, June 26). Dr Eric Topol: Preparing the healthcare workforce to deliver the digital future.Video File. Retrieved from https://youtu.be/f2UTxtgSFzU

IBM Think Academy. (2015, May 20). How It Works: IBM Watson Health. [Video File]. Retrieved from https://youtu.be/ZPXCF5e1_HI

Intel AI. (2018). Predictive analytics in health care. White Paper. Santa Clara, CA: Intel Corporation. Retrieved from https://www.intel.ai/nervana/wp-content/uploads/sites/53/2018/05/Predictive-Analytics-in-Healthcare.pdf

O’Connor, A. (2019, March 11). How Artificial Intelligence Could Transform Medicine. The New York Times. Retrieved from https://www.nytimes.com/2019/03/11/well/live/how-artificial-intelligence-could-transform-medicine.html

Patil, A. (2019, Jan. 15). AI and predictive analytics lead to improved delivery of healthcare services. Healthcare Business & Technology. Retrieved from https://www.healthcarebusinesstech.com/ai-and-predictive-analytics-lead-to-improved-delivery-of-healthcare-services/

Personalized Medicine Coalition. (n.d.). The Age of Personalized Medicine. Retrieved from http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/pmc_age_of_pmc_factsheet.pdf

Research Square. (2019, August 21). 3D printing for biomedical applications. Video File. Retrieved from https://youtu.be/vk6ALcQiZ2w

Sami, M. (2017). The Evolution of Analytics | The 5 Types of Analytics. Retrieved from https://melsatar.blog/2017/07/30/the-evolution-of-analytics/

The Medical Futurist. (2018, May 14). The Ultimate List of What We Can 3D Print in Healthcare – The Medical Futurist.Video File. Retrieved from https://youtu.be/9XYLRaVqzNY

WeirdGeek. (2018). 5 Types of analytics: Prescriptive, Predictive, Diagnostic, Descriptive and Cognitive Analytics.Retrieved from https://www.weirdgeek.com/2018/11/types-of-analytics/