Today, an increasing number of healthcare providers are embracing analytics as part of their efforts to enhance the quality of care that they deliver. In addition to improving quality and safety, healthcare analytics has also been linked to better patient satisfaction and higher operational efficiency. Furthermore, as they use data to inform their decisions, providers can expect to significantly lower the cost of care. Given the numerous benefits that healthcare analytics delivers, one is forced to wonder why there are some providers who are either reluctant or seem unable to adopt analytics. The answer lies in the various challenges that are encountered as providers attempt to incorporate healthcare analytics into their operations.
Cost optimization and improvements in quality of care are some of the key benefits of healthcare analytics (Davenport, 2014). While they are critical to healthcare delivery, these benefits do not distract from the many drawbacks and challenges that firms face when they attempt to embrace analytics. Privacy concerns are among the main challenges that are hampering the adoption of healthcare analytics (Abouelmehdi et al., 2017). If they implement healthcare analytics, providers will need to use patient data as part of their efforts to improve care. While the data offers them an opportunity to improve their services, it also presents risks. Today, cybercrime has become a grave challenge for providers. There have been various attacks which have inspired concern and fear among healthcare organizations. These attacks discourage the adoption of healthcare analytics as the providers worry about the invasion of the privacy of their patients. In addition to being concerned about privacy violations, the providers’ apprehension is also the result of the heavy penalties that they could face under the strict regulations that the US has enacted to safeguard patient safety and privacy. Another issue that discourages providers against adopting healthcare analytics concerns the lack of the frameworks, policies and strategies needed for successful implementation (Ginsburg et al., 2018). In order to effectively integrate healthcare analytics into their operations, healthcare organizations need the appropriate resources and frameworks that analytics requires for smooth operations.
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Storage, security, and inadequate managerial skills are other factors that have been blamed for the slow pace of the implementation of healthcare analytics (Kruse et al., 2016). The adoption of analytics requires that providers should have storage facilities where they hold data and implement stringent security protocols. While they facilitate analytics, these measures are capital intensive and can be prohibitively costly. Furthermore, the providers are required to have managers who have the skills needed to fully operate analytics. Without the skills, storage and security, it is nearly impossible for the adoption of analytics to occur. It is little wonder that many healthcare institutions are facing serious challenges. The fact that the insights obtained from analytics could diminish the role of human intelligence in decision making is another challenge that healthcare organizations grapple with (Amarasingham et al., 2014). There is no doubt that healthcare analytics presents numerous advantages that range from efficiency to improved safety and quality of service. However, despite these advantages, analytics cannot replace human wisdom and intelligence. Healthcare professionals possess vital skills and intelligence that they have gained through years of service. These professionals are concerned that analytics will render their skills unnecessary and unimportant.
The PICO model can be used to capture the hardships that providers are facing. Using this model, one is able to determine how the problems discussed above affect patients, the interventions that can be implemented and the outcomes that can be expected. The PICO question can be framed as follows: what initiatives can healthcare institutions adopt in their quest to eliminate the various hurdles that they encounter as they attempt to embrace healthcare analytics? By developing a response to this question, the institutions can identify the solutions whose implementation will allow them to overcome the struggles that they continue to encounter. It is important to note that the institutions cannot solve the challenges alone. They require the full support of other stakeholders who wish to see improvements in the quality of care delivered and significant drops in the cost of services.
References
Abouelmehdi, K., Ben-Hssane, A., Khaloufi, H., & Saadi, M. (2017). Big data and privacy in healthcare: a review. Procedia Computer Science, 113, 73-80.
Amarasingham, R., Patzer, R. E., Huesch, M., Nguyen, N. Q., & Xie, B. (2014). Implementing electronic health care predictive analytics: considerations and challenges. HealthAffairs. DOI: https://doi.org/10.1377/hlthaff.2014.0352
Davenport, T. H. (2014). The value life cycle.
Ginsburg, P. B., Loera-Brust, A., Brandt, C., & Durak, A. (2018). The opportunities and challenges of data analytics in health care. Brookings Institution. Retrieved January 22, 2019 from https://www.brookings.edu/research/the-opportunities-and-challenges-of-data-analytics-in-health-care/
Kruse, C. S., Goswamy, R., Raval., Y., & Marawi, S. (2016). Challenges and opportunities of big data in health care: a systematic review. JMIR Medical Informatics, 4 (4), e38.
