Data Analytics in Healthcare
Healthcare analytics aims to make healthcare effective, affordable, accessible, and patient oriented,
benefiting both the patients and the healthcare providers. Data abounds in the healthcare industry.
Leveraging data analytics to uncover previously hidden insights from these data can transform and add
value to the healthcare industry, from reducing costs to enhancing patient care to predicting health risks.
Enhancing Patient Care and Satisfaction
Healthcare analytics can enhance patient care in myriad ways. To develop better treatment plans for
asthmatics, data is collected from inhalers with GPS-enabled trackers to identify asthma trends on both
the individual and population level (Lebied, 2020).
Visualization tools and analytics also help optimize workflow and staffing in clinics and hospitals by
anticipating spikes in patient flow or identifying times of low utilization. Ensuring that the supply of
doctors and nurses throughout the day matches the patient demand reduces wait time and boosts
patients’ satisfaction (HealthITAnalytics, 2019).
Reducing Costs
Data analytics can help reduce costs for both patients and healthcare providers. It enables quick,
precise diagnosis which translates to faster time to treatment. For example, people with chronic
conditions can use bio-sensing which automates monitoring and detects real time changes in an
individual’s health status. Data analytics tools connected to these bio-sensing wearables can provide a
preventive approach through real-time information to support early diagnosis and prevent crises. If a
negative change occurs, patients and care givers can be alerted quickly, preventing deterioration, and
reducing hospitalization and emergency admissions (Three Ways Data Analytics Can Help Reduce
Healthcare Costs, 2018).
Predicting Risks and Providing Personalized Treatment
Data analytics can predict the risk of certain diseases. For instance, data analysis of patients’ blood
samples allowed scientists to obtain certain markers identifying those infected with the Zika virus and
provide accurate diagnosis. In addition, there are machine learning algorithms that uses large datasets
of patients’ biological information to detect autoimmune diseases well before they become acute and
start to show definite symptoms (Spurlock, 2018).
Data analytics and modelling lets healthcare providers predict a patient’s response to certain treatments
by comparing the results of previous patient cohorts with a similar genetic profile, to ultimately discover
which therapy offers the best chance of success (HealthITAnalytics, 2019).
Data Analytics in Consulting
Data analytics transforms many industries, and consulting is no exception. At Accenture, a multinational
professional services company, analytics drives decision making. Accenture uses Win Probability Tool
which aggregates many years’ worth of deals, geographical information, price points and other metrics
to score the likelihood of winning potential business opportunities with a 90% accuracy. Moreover,
analytics dashboards provide executives with visualizations about real estate utilization, detailing the
use of seats and meeting rooms in Accenture offices around the world. Says Wilson, Accenture’s former
CIO, “Getting the right utilization in terms of space and technology is key. We have to be very careful
of how we allocate fixed real estate and office space." Thus, data analytics supports important decision-
making to improve Accenture’s employee experience (Boulton, 2020).
Conclusion
In conclusion, data analytics is invaluable in helping to run, grow, and transform businesses across
industries.
References
Healthcare analytics aims to make healthcare effective, affordable, accessible, and patient oriented,
benefiting both the patients and the healthcare providers. Data abounds in the healthcare industry.
Leveraging data analytics to uncover previously hidden insights from these data can transform and add
value to the healthcare industry, from reducing costs to enhancing patient care to predicting health risks.
Enhancing Patient Care and Satisfaction
Healthcare analytics can enhance patient care in myriad ways. To develop better treatment plans for
asthmatics, data is collected from inhalers with GPS-enabled trackers to identify asthma trends on both
the individual and population level (Lebied, 2020).
Visualization tools and analytics also help optimize workflow and staffing in clinics and hospitals by
anticipating spikes in patient flow or identifying times of low utilization. Ensuring that the supply of
doctors and nurses throughout the day matches the patient demand reduces wait time and boosts
patients’ satisfaction (HealthITAnalytics, 2019).
Reducing Costs
Data analytics can help reduce costs for both patients and healthcare providers. It enables quick,
precise diagnosis which translates to faster time to treatment. For example, people with chronic
conditions can use bio-sensing which automates monitoring and detects real time changes in an
individual’s health status. Data analytics tools connected to these bio-sensing wearables can provide a
preventive approach through real-time information to support early diagnosis and prevent crises. If a
negative change occurs, patients and care givers can be alerted quickly, preventing deterioration, and
reducing hospitalization and emergency admissions (Three Ways Data Analytics Can Help Reduce
Healthcare Costs, 2018).
Predicting Risks and Providing Personalized Treatment
Data analytics can predict the risk of certain diseases. For instance, data analysis of patients’ blood
samples allowed scientists to obtain certain markers identifying those infected with the Zika virus and
provide accurate diagnosis. In addition, there are machine learning algorithms that uses large datasets
of patients’ biological information to detect autoimmune diseases well before they become acute and
start to show definite symptoms (Spurlock, 2018).
Data analytics and modelling lets healthcare providers predict a patient’s response to certain treatments
by comparing the results of previous patient cohorts with a similar genetic profile, to ultimately discover
which therapy offers the best chance of success (HealthITAnalytics, 2019).
Data Analytics in Consulting
Data analytics transforms many industries, and consulting is no exception. At Accenture, a multinational
professional services company, analytics drives decision making. Accenture uses Win Probability Tool
which aggregates many years’ worth of deals, geographical information, price points and other metrics
to score the likelihood of winning potential business opportunities with a 90% accuracy. Moreover,
analytics dashboards provide executives with visualizations about real estate utilization, detailing the
use of seats and meeting rooms in Accenture offices around the world. Says Wilson, Accenture’s former
CIO, “Getting the right utilization in terms of space and technology is key. We have to be very careful
of how we allocate fixed real estate and office space." Thus, data analytics supports important decision-
making to improve Accenture’s employee experience (Boulton, 2020).
Conclusion
In conclusion, data analytics is invaluable in helping to run, grow, and transform businesses across
industries.
References
