Challenges and Opportunities in Healthcare Analytics Adoption

The assignment content discusses the importance of data analytics in healthcare, specifically in children's hospitals where it can help reduce costs and improve patient outcomes. The healthcare analytics adoption model is introduced as a framework for evaluating an organization's progress towards adopting analytics capabilities. It provides a roadmap for industries to measure their actual progress towards analytics adaption.

Running Head: HEALTHCARE DATA ANALYSIS
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Healthcare data analysis
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HEALTHCARE DATA ANALYSIS
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Table of Contents
Importance of the project...........................................................................................................2
Purpose of the analysis...............................................................................................................2
Benefits of Data analysis to the patient......................................................................................2
Cost related benefits of data analysis.........................................................................................3
Healthcare Analytics Adoption Model.......................................................................................3
References..................................................................................................................................4

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The project has been conducted to provide an effective and beneficial data analysis
that may help the health care providers to achieve t the health goals already set for the
patients. And predicted the issues that are commonly occur in most patients. The health care
data analysis is the term in health care that describes the analysis activities that can be
achieved by after collecting the patient data form four different areas of health care that are
cost data, clinical data, patient sentiment and behaviours, pharmaceutical and the research
and development data. The data analysis projects allows the health care professional to
examine the patterns in different healthcare data to identify how the clinical care can be
improved and how decrease the barriers of health care that impacts the safety and care of
patient. The data analysis projects help the health care professional to find out the history of
the patient and the common re and different reason of particular disease or infections, or in
the epidemiological studies (Lopez, Mathers, Ezzati, Jamison, & Murray, 2006).
Purpose of the analysis
Data analysis is not only beneficial for the health care industry but also useful in
different other industries for many years. There are various benefits of data analysis for the
patients such as improving care of the diseased person. It helps the health care professional to
get information’s of the particular patient in a very short period of time that ultimately
reduces the time of care and provide better care to the patient. It allows the healthcare
workers to get the correct and every information’s related to the patient (Bradley, Curry, &
Devers, 2007).
Benefits of Data analysis to the patient
Data analytics is the method that helps the healthcare entities to remind patient to
continue with the healthy lifestyle and keep the current track where the diseased person

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stands in relation to their lifestyle choices. These data analysis projects can also be used to
provide the data or information that can help the patient to enhance their lifestyle. This
ultimately makes the person’s health a major priority. Data analysis helps the administrators
and the clinical providers to fulfil the difference between the services provided to the patient
(Groves, Kayyali, Knott, & Van Kuiken, 2013).
The availability of the patient’s data availability on single platform help for the
informed and beneficial decision making when it needs to be done in emergencies. This will
directly provide good results and more person cantered care. The patient experiences,
engagement and the relationship with them in enhanced by using data analysis. Data analysis
also used to provide and constantly monitor the patient’s vital signs. This ultimately works as
an alert that needs to be share with the health care providers in any adverse condition of the
diseased person. The data analytics also help in improving the patient’s engagement in the
treatment process. In the modern world the patients have a great interest in using smart
devices and keeping record of their health on every step such as their sleeping habits, heart
rate and recommended diet, precautions. This can be achieved by the big data analytics. Data
analysis can also help the health care providers to select the most favourable treatment
strategies for the patient with the predictive analytics, this ultimately help the patient to get
accurately determined treatment plans which is based on theirs medical history and the
demographic data (Raghupathi, & Raghupathi, 2014).
Cost related benefits of data analysis
Data analysis is also beneficial for the health care organisation to the treatment costs.
Reducing the administrative cost is the major challenge health care providers’ face in this
industry. One fourth of entire healthcare budget spent on the administrative costs, as they
need human resources to perform the different tasks. The medical records information

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exchange might help them to reduce these costs (Nambiar, Bhardwaj, Sethi, & Vargheese,
2013). One of the example of organisation benefited by data analysis each year is Beaufort
Memorial hospital in the South Carolina, they saves nearly $435,000 annually by discharging
the patients 12 hours earlier, which ultimately reduces the management costs and patient
costs (Bradley, Curry& Devers, 2007). Big electronic data and its analysis can also help the
organisation reducing costs by eliminating the unnecessary testing due to lack of patient data.
One of the examples of this benefit is St. Louis Children’s Hospital; they are able to decrease
the tests costs $6,000 orders for the Dravet Syndrome which is the rare type of epilepsy by
using the data analysis. The physicians can also use the predictive data analytics to help build
more adequate and accurate instant diagnosis (Wang et al., 2003). For examples a patient
enters the ICU for chest pain can be examined instantly by the doctors using the diagnostic
tool which is based on the predictive analytics to identify whether the patient needs hospital
admittance or not (Hillestad et al., 2005). If proper data analysis is used then it will not only
provide complete set of details of the patients to health check-up hospital but also strengthen
the overall patients care and their treatment.
Healthcare Analytics Adoption Model
The healthcare in the world is slowly progressing in data management such as data
collection, sharing of data, and data analytics. The collection of data and the sharing waves of
these data by the crucial deployment of the electronic health information or records transfer
have failed to impacts the cost and quality of healthcare.

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The healthcare analytics adaption model has been developed as the guide to
differentiate groups of analytics capabilities, and delivering a systematic analytics for the
healthcare organisations to collect, store and share the data. It basically evaluates the
adoption of analytics of and organisation (Sanders, Burton, & Protti, 2013). It provides a
roadmap for the industries to measure the actual progress in towards the analytics adaption. It
is the framework that evaluates the vendor products. This model basically borrows the
lessons, learned from other models like HIMSS EMR adoption model. It identifies the
equivalent approach to evaluate the adaption of the analytics within healthcare. This model
used by the organisations to progress in using the data sophistically to notify decision
making. Its main purpose is to serves as the guiding tool for the health care providers
(Venkatraman, Sundarraj, & Seethamraju, 2015). This above given model has revealed that
if patient is provided complete information about his health then they could keep better health
check-up assessment. It will also set up proper health assessment.

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References
Bradley, E. H., Curry, L. A., & Devers, K. J. (2007). Qualitative data analysis for health
services research: developing taxonomy, themes, and theory. Health services
research, 42(4), 1758-1772.
Bradley, E. H., Curry, L. A., & Devers, K. J. (2007). Qualitative data analysis for health
services research: developing taxonomy, themes, and theory. Health services
research, 42(4), 1758-1772.
Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The ‘big data’revolution in
healthcare. McKinsey Quarterly, 2(3).
Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., & Taylor, R. (2005).
Can electronic medical record systems transform health care? Potential health
benefits, savings, and costs. Health affairs, 24(5), 1103-1117.
Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T., & Murray, C. J. (2006). Global and
regional burden of disease and risk factors, 2001: systematic analysis of population
health data. The Lancet, 367(9524), 1747-1757.
Nambiar, R., Bhardwaj, R., Sethi, A., & Vargheese, R. (2013, October). A look at challenges
and opportunities of big data analytics in healthcare. In Big Data, 2013 IEEE
International Conference on (pp. 17-22). IEEE.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), 3.
Sanders, D., Burton, D. A., & Protti, D. (2013). The healthcare analytics adoption model: A
framework and roadmap. Health Catalyst.

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Venkatraman, S., Sundarraj, R. P., & Seethamraju, R. (2015). Healthcare Analytics
Adoption-Decision Model: A Case Study. In PACIS (p. 51).
Wang, S. J., Middleton, B., Prosser, L. A., Bardon, C. G., Spurr, C. D., Carchidi, P. J., ... &
Kuperman, G. J. (2003). A cost-benefit analysis of electronic medical records in
primary care. The American journal of medicine, 114(5), 397-403.