Becoming a Data-Driven Healthcare Organization
The healthcare industry has an enormous amount of data generated from various researches, patient’s diagnosis, hospital record systems, aged care, and many other related services and products. Though data science does not replace human intelligence, it supports the decision process. More investments in Health IoT devices, data storage and healthcare analytics has capabilities of massive impact in the healthcare sector. Researchers argue that the industry is not making use of capabilities of Data Science in a way they should be using it (Wills, M.J., 2014). Researchers also fear that most of the academic research is technology-oriented and not related to the strategic implications (Yichuan Wang, LeeAnn Kung, Terry Anthony Byrd, 2018). This document explores some of the opportunities in the healthcare industry and challenges in accepting them.
Data Science for Data-Driven Healthcare
Life expectancy has been continuously increasing over the decades and therefore the expectations from the healthcare industry (Health, United States, 2001). Traditionally healthcare industry has been into curative healthcare, but as the science advanced and expectations started building up, healthcare is now also into preventive and maintenance segments. Preventive healthcare, as the name suggests, is to make sure that the body does not catch any diseases or infections. There are several over the counter medicines, vitamins and food supplements available to make sure the body needs are met. If a person has some infection or disease, then making sure that it does not worsen and taking precautionary supplements for that is maintenance healthcare. An example of maintenance medication in healthcare is the insulin dose for the patients who are diabetic. It makes sure that the blood sugar does not go beyond or below the acceptable limit. Unconventional has already become conventional and more focus is now diverted towards preventive healthcare (Australian Government, Department of Health, 2019).
Modern Examples of Data Analytics in Healthcare:
ElderSense has launched Adult Diapers with sensors that can notify precisely when the diaper needs to be changed (BusinessWire, 2018). Another organization is expanding this further and working on a product for hospitals, where the central display board would have notifications for caretakers to change it before or on time. Researchers have also explored real-time analytics opportunities using an RFID-enabled platform for hospital management with RFID tags attached to each patient and asset in the hospital (Chun-Hung Cheng, Yong-Hong Kuo, 2016).
Preventive Healthcare and Personal Medicine
Investment in preventive healthcare is important for the well-being of the population, so that, the occurrence of diseases and disabilities is avoided or prolonged. Income and use of preventive healthcare are positively correlated and hence it is beyond the reach of underprivileged (Fuhmei Wang., 2018).
“In personal medicine, predictive analytics can play a key role at the individual level and enable the use of prognostic analytics and big data to allow for doctors and other involved parties to find cures for certain diseases which they might not be familiar with at a given time.”
- Deloitte- Kylie Watson, 2019
Many smartphone users have wearable devices to keep track of the daily activities including sleep, calories, heart rate and other health parameters. Tracking personal health parameters could help in regulating them. Researchers claim that such devices could also help in controlling healthcare costs through preventive healthcare and enhance the quality of life (Simpao, A.F., Ahumada, L.M. and Rehman, M.A., 2015). The study also shows that 66% of consumers are willing to wear biosensors to track their health and activities (IBM Watson Health, 2018). Individuals’ data may not speak a lot, technology has the potential to combine this data from multiple users and provide insights to improve health. Further data collection on health factors and diseases can create opportunities for informed decision making for curation.
Maintaining Health with Health Trackers
As life expectancy has increased, so is the health of an individual at a certain age. Studies show elders with better health conditions have longer life expectancy than those in poor health states with similar cumulative healthcare expenditure of lifetime (Lubitz, J., Cai, L., Kramarow, E. and Lentzner, H., 2003). Devices for blood pressure, blood sugar, heart rate monitoring are not a novelty anymore. Data analytics has the capability of improving the links between this vast data and doctors for better treatment.
Glooko is an app that allows people with diabetes to sync their blood glucose readings from 25+ popular meters directly to supported Apple and Android devices. Using the data, they can adjust their diet, insulin, and other medications accordingly.
- Barrie Davenport,2015
Real-time analysis can also help in saving lives and decreasing curative healthcare costs. IoT and big data solutions to capture this information in the right format and using it to analyze individuals’ health trends can lead to avoiding health consequences. Further enhancements in health trackers can help develop a complete eco-system from preventive to maintaining and from maintaining to curative healthcare.
Curative Healthcare with Informed Decision Making
Analyzing historical and real-time admittance with big data analytics would lead to cost-saving through optimizing business operations and staff performance. IoT, visual analytics, predictive analytics and segmentation techniques can help in diagnosing patients’ discomfort and diseases in early stage to provide customized treatment. It can lead to a better and smoother experience for the patients and healthcare service providers. Holistic data collection about patients is one of the areas to be explored by big data experts. This data can be used in preventive as well as predictive treatments. Recently, a blood test has been developed to predict risk of death in next 5–10 years (Medical Daily, 2019).
Data Analytics, Enabling Remote Care
With increased life expectancy, the number of elders in aged care is growing exponentially (JAPARA, 2017). Most do not track health attributes as frequently as they should unless the caregivers take responsibility for that. Also, the obvious signs of discomfort are ignored initially which worsens the situation and, hence confounding the curative treatment.
“Certain studies have estimated that between 45% and 80% of residents in aged care facilities have substantial pain that is undertreated.”
-Mundipharma, 2017
With advanced IoT and wearable devices, users’ healthcare networks can be created in which the user himself can track the parameters along with his/her close circle and nearby healthcare providers, creating a personalized healthcare ecosystem. If any discrepancies are observed, close circle and healthcare service providers could be notified. Preventive and predictive healthcare solutions developed based on the historic data can significantly change the healthcare approach in the rural areas where there may not be experienced healthcare providers available for diagnosis. (Nemet, G.F. and Bailey, A.J., 2000.)
Positives of Usage of Data Science in Pharmaceutical Research
Data analytics has already been helping researchers for decades in drug efficacy tests, selecting the right candidates for research, developing new and innovative products for medical purposes and so on. Increased capabilities of data analytics can accelerate research activities, predict drug efficacy with better accuracy and reduce costs. (Simpao, A.F., Ahumada, L.M. and Rehman, M.A., 2015) Inexpensive preventive healthcare would lead to easy maintenance and higher life expectancy.
Impact and Challenges in this Model
“When big data is synthesized and analysed — and those aforementioned associations, patterns and trends revealed — healthcare providers and other stakeholders in the healthcare delivery system can develop more thorough and insightful diagnoses and treatments, resulting, one would expect, in higher quality care at lower costs and in better outcomes overall.”
Raghupathi, W. and Raghupathi, V., 2014
Variety, veracity, velocity and volume of data has created opportunities for data-driven decision making and healthcare industry is not an exception. Some of the opportunities and risks are captured very well by Deloitte:
Big Data in Healthcare
The healthcare industry has been generating enormous data for decades, but most data is in the form of hard copies. Digitizing this data would create great opportunities for analytics but the volume, variety and veracity of the data and capabilities to digitize it, remains a challenge, until further advancements in the technology. “Reports say, data from the U.S. healthcare system alone reached, in 2011, 150 exabytes. At this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (1021 gigabytes) scale and, not long after, the yottabyte (1024 gigabytes)” (W. Raghupathi & V. Raghupathi, 2014). Unfortunately, the newly generated data doesn’t always come from the same source and does not have the same format, increasing the complexity of storage and curation. Some initiatives have already been taken to record information about rare conditions globally, but it may not include all the conditions.
Questionable Data Integrity
Historical data is being digitized, but the accuracy and extensiveness of that data are questionable. Research also shows that the data collected by wearables is not always accurate (Furberg, R.D., 2016). Reports are questioning the integrity of historical data as cases were observed where only 23.5% of the details matched with what the patient had indicated (Thomas Beaton, 2017). Inaccurate and incomplete data may lead to erroneous analytics hence AI cannot and should not replace human intelligence.
Data Privacy of Sensitive Data and Legal Constraints
Changes in the healthcare system cannot be done by a single healthcare provider or a country. It should be a joint effort by multiple health service providers globally, to come together and build this ecosystem for betterment of human being. The majority of healthcare data remains to be sensitive data and can be hacked without proper security measures. Data privacy laws of some countries and regions may create challenges as the sensitive data cannot go out of the territory (Katie Yahnke,2018).
Data Science Limitations
All the analytics models come with certain accuracy and there is always a chance of false-positive or true-negative. AI needs to be used with the utmost care when making decisions about treatments. Study shows that the guidelines for decision handover from machine to human intelligence are blurred and unregulated (Deloitte- Kylie Watson, 2019). Kylie also points at ethical and moral hazards while using data science in the healthcare industry.
Conclusion
The impact data science usage can have on the healthcare industry is enormous. IBM has predicted that precision medicine will develop as a $96 billion market. (IBM Watson Health, 2018) Healthcare industry needs to evolve by overcoming all the challenges for the betterment of human being and to meet expectations from the industry. For a bigger impact, individual healthcare providers need to initiate data-driven decision making at the institution level and building it further with collaboration with other institutes. With secured databases, data needs to be collected with care. Countries need to amend laws to make sure the healthcare data is shared for research, decision making. Not only big data but smart data in healthcare, with supportive high-performance computing and advanced storage capacities, has the power to change the current state of healthcare splendidly.
References
The New Daily, 2017, life-expectancy-australia
Health, United States, 2001: with urban and rural health chart book. Hyattsville, Md.: National Center for Health Statistics, 2001. (DHHS publication no. 01–1232.)
Deloitte- Kylie Watson, 2019 predictive-analytics-health-care-value-risks.html
IBM Watson Health, 2018 https://www.youtube.com/watch?v=qtEvafANP3I
BusinessWire, 2018 ElderSens-Releases-DiaperSens-Smart-Diaper-Sensor-Wet
Wills, M.J., 2014. Decisions through data: Analytics in healthcare. Journal of Healthcare Management, 59(4), pp.254–262.
Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), p.3.
Simpao, A.F., Ahumada, L.M. and Rehman, M.A., 2015. Big data and visual analytics in anaesthesia and health care. British journal of anaesthesia, 115(3), pp.350–356.
Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, pp.3–13.
Cheng, C.H. and Kuo, Y.H., 2016. RFID analytics for hospital ward management. Flexible Services and Manufacturing Journal, 28(4), pp.593–616.
Lubitz, J., Cai, L., Kramarow, E. and Lentzner, H., 2003. Health, life expectancy, and health care spending among the elderly. New England Journal of Medicine, 349(11), pp.1048–1055.
Furberg, R.D., 2016. Known Unknown: The Uncertainty and Inaccuracy of Consumer Wearable Devices. J Aerobics Fitness, 1, p.e109.
Nemet, G.F. and Bailey, A.J., 2000. Distance and health care utilization among the rural elderly. Social Science & Medicine, 50(9), pp.1197–1208.
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Thomas Beaton, 2017 Mismatched Symptoms Call EHR Data Integrity into Question
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