Executive summary
The Carolinas Healthcare System (CHS), to support its strategic roadmap and goal, has invested in data analytics. Using data analytics to provide CHS with insights that can be implemented during pilot initiatives has proven to be fruitful. CHS has needed help to balance the need to protect patient anonymity and collect data that may be used for the progress and success of its data analytics. CHS might benefit from more data pieces not supplied in this case instance to improve its operations. It would have been possible to obtain the data that was lacking through surveys and interviews, and it would have been possible to analyze the data by applying descriptive statistics and machine learning methods. Dr. Michael Dulin and his team should investigate the possibility of forming strategic partnerships with organizations in the greatest position to deliver individualized, integrated data management services to patients. This study suggests that CHS strengthen its data management and analytic skills to facilitate further the process of making strategic decisions.
Data Analytics and CHS’s Mission
CHS has invested in data analytics to bolster its strategic roadmap and achieve its healthcare system objective, which is to enhance the health of those it serves for the good of the people and the general organization. Utilizing data analytics, CHS can better comprehend patients’ requirements and deliver individualized care, which will improve its outcomes. CHS needed analytical skills from an industry player that customers would trust to incorporate their healthcare data in the future as a result of the introduction of consumer tech businesses into the healthcare market; as a result, there was an entry of consumer tech companies into the healthcare field; thus, they decided to go with DA to fill the existing gap in the healthcare field. The Department of Agriculture (DA) has as continuing strategic priority the ability to foresee health needs, continuously improve patient outcomes, and develop revolutionary solutions for addressing community health challenges which CHS can utilize effectively for their upgrade.
Da had successfully launched many pilots encompassing a wide range of medical ailments, geographical areas, and functional capabilities, thus capturing the attention of CHS in furthering its programs. Instead of working on expanding its workforce, Carolinas Health Services (CHS) decided to form a partnership with the Department of Agriculture (DA) to focus on enhancing patient outcomes instead, which proved to be successful and of value to the company. Because of this, the mission states that “Our customers are the consumers and patients first, with payers coming in second” (Quelch & Rodriguez (2015). Most of DA’s capability was put toward delivering tools to CHS-affiliated hospitals so that these facilities could serve patients with healthcare that was on par with the best in their field. Analytical methods for evidence-based population health management, individualized patient care, and predictive modeling were created by DA, and CHS can effectively utilize the programs.
CHS has, via data analytics, uncovered possibilities to trial solutions that enhance patient outcomes while reducing costs. CHS, for instance, used data analytics to determine whether patients posed a significant danger of being readmitted and then designed individualized treatments to cut down on readmission rates. Data analytics have successfully achieved CHS’s goal of supporting the organization’s purpose. It helped the organization move away from a culture that was focused on anecdotes and toward one that was evidence-based, built a structure for the governance of data, and gathered and managed massive volumes of data efficiently. Many people saw the possibility that DA could become an additional source of revenue in the future by outsourcing the analytics services it provides to third parties. DA, clinical and translational studies, information technology, human resources, accounting and the auditing and accounting systems business, and the workplace of the general counsel were all represented on the data governance council that CHS formed.
Following the launch of the DA, the team received more than twice as many requests as it could accommodate due to capacity constraints. As a consequence of this, a procedure for prioritization using predictive analytics was developed. Instead of focusing on expanding the number of CHS as a measure of success, the DA2 wanted to see an increase in the services’ quality. DA was dedicated to cultivating excellent connections with the nurses and physicians at the hospital. According to Quelch and Rodriguez (2015), the decision-making process might be aided by combining the patient and financial data collected from the EMR through analytics. The data acquired through the CHS network collected at many different sites of care was used, and clinicians incorporated any additional suggestions and tools that were necessary. CHS took advantage of strategic alliances to integrate data from providers and payers and data from consumers into its predictive models. The statistics on expenditure were used to generate a risk score for patients who were hospitalized, and this score was then given to physicians and other healthcare professionals so that they might reprioritize the delivery of care. The communication approach that DA2 used was centered on improving the quality of the results through the engagement of the patient in changing his or her behavior.
CHS has struggled to find a happy medium that maintains patient anonymity while providing useful data. According to Quelch and Rodriguez (2015), most businesses have realized the value of analytics in expanding access to healthcare and reducing associated costs. CHS has established a set of policies and processes for data governance to guarantee that patient data is maintained securely and following the obligations imposed by regulatory authorities. On the other hand, certain personnel may not comprehend the regulations entirely, which could violate the patient’s right to privacy. In order to avoid illegal access to patient data, CHS workers need to be provided with training on data governance rules and procedures, and data access controls need to be implemented across the organization.
Data missing from the case
Staff management is another data-driven insight that will assist CHS in providing quality patient care following the business’s mission. With an engaged and unified workforce, service rates will stay high, patient-centered care will stay high, and medical mistakes will be more likely to occur. Data analytics, on the other hand, makes it possible to streamline staff management. When institutions are pressed for time, they can optimize their workforce while also anticipating the theater’s needs. Uneven personnel distribution is a common problem that plagues the mobility of healthcare institutions. Because of the imbalance between the departments, one department may need more personnel when required the most. Because of this, there is a danger of decreased job motivation and increasing absence rates.
Information on the performance of employees is necessary for staff management. The average yearly absenteeism and the departmental labor effectiveness over the preceding five years are important factors to consider. For the sake of data gathering, it is recommended that workers use computer systems to check in and check out of their shifts. Absenteeism is when an employee does not show up to work as scheduled and does not provide a valid reason for their absence. As a result, there is already data accessible on absenteeism. The return on investment generated by each department is used to calculate the overall labor effectiveness. At the end of the year, the productivity of each division in the firm is evaluated using a standardized rubric. It is documented over the preceding five years and is broken down by division. The process of data analysis begins with the collection of raw firm data from the various sources described earlier. The data has been categorized based on absenteeism and work efficiency during the past five years. There will also be an examination of the labor efficiency of each department. With data visualization, the CHS will be able to determine in advance, for particular departments and notably during busy seasons, whether or not they will require more people. When there is a lull in one department, moving knowledgeable employees to another may be beneficial. In addition, studies of medical data provide operators and senior staff members with the ability to give help when required.
Recommendation
Dulin is trying to decide which pilot program to continue with, even though there is significant related uncertainty. It is necessary for the program to both improve its flexibility and lower its expenses. In addition, it is necessary to translate and convert the expenses connected with DA2 into advantages by boosting CHS’s internal efficiency. Outside of CHS, DA2 has the potential to become a very useful tool. Because there are so many opportunities available in the wider market, it has the potential to become a source of revenue for the business. DA2 has the potential to both enhance results and simultaneously cut down on waste. However, Dulin is also responsible for meeting as many of its internal requirements as is humanly possible, including concerns about users’ privacy (Quelch & Rodriguez, 2015).
The HIPAA’s privacy provision safeguards any patient information that may be used to identify the patient. HIPAA regulates the healthcare providers, “clearinghouses, ” and payers. Patient information is only used for healthcare procedures, payment, or treatment with the patient’s agreement unless the data has been de-identified (meaning that identifying information is not shown). HIPAA does not prohibit data used to conduct analyses intended to make changes to the healthcare system. Access to patient information can also be granted to other parties, such as healthcare payers, whom HIPAA also covers. Because CHS was a healthcare provider and its workers had insurance they paid for themselves, the firm could not view patients’ disaggregated data. In addition, the human resources department gathered employee data, but this information was kept separate from any information on their health (Quelch & Rodriguez, 2015). Serious legal sanctions, including a punishment of at least one million dollars, might arise from violating the HIPAA confidentiality requirement if it is not followed.
Regarding wearables (such as step trackers and heart rate checkers), technology giants like Google and Apple have built mobile platforms that collect and aggregate individual data. With the user’s permission, the Health kit (an Apple device) might display the laboratory test results in a dashboard. If these digital companies establish an EMR firm, they will have access to clinical data they otherwise would not have. Because of their focus on raising public awareness, education centers are ideally situated to manage high volumes of patient traffic (Hunter & Gooedie, 2010). Utilizing the obtained data to identify the prevalent illness may be possible. Because of the high volume of patients treated there, these facilities generate large volumes of data that might be integrated into data management systems to improve efficiency in patient care. Almost every business that uses patient segmentation has access to a corporate data warehouse and employs scalable data methodologies that may provide integrated data management.
Without a new business model, Dulin must decide which project to pursue over the next three years. Dulin must first get senior-level buy-in, organizational alignment, and available resources. All pilot programs were consistent with CHS’s goal of providing better care with a focus on the individual. However, more than technological investments are appropriate. Alterations should be made to how things are done and how people think within the company. Dulin would also need to determine the extent to which the existing resources will contribute to the success and efficiency of the project (Kaiser, El Arbi, & Ahlemann, 2015). The dedication of upper management is crucial in breaking down obstacles in the project and should be considered. Dulin must also establish early on what will constitute a successful project. Such metrics are essential for monitoring the development of a project.
The danger of CHS spending money it does not have due to planned initiatives can be mitigated with accurate cost projections. Salaries, supplies, machinery, and overhead are all factored into these estimates (Lohrey, 2019). Assumptions made may include expenditures for labor that do not rise or fall as the project progresses, no change in total project expenses during the duration of the project, and no increase in the price of materials used in the course of regular project operations
A project’s return on investment (ROI) can be used as a performance metric and must be positive. To calculate return on investment, use the following formula (Lohrey, 2019):
ROI= [(Financial value- project costs)/project cost]*100
it is necessary to add up all costs and the projected income to establish the project’s profitability.
References
Hunter, C. L., & Goodie, J. L. (2010). Operational and clinical components for integrated-collaborative behavioral healthcare in the patient-centered medical home. Families, Systems, & Health, 28(4), 308.
Kaiser, M. G., El Arbi, F., & Ahlemann, F. (2015). Successful project portfolio management beyond project selection techniques: Understanding the role of structural alignment. International journal of project management, 33(1), 126-139.
Lohrey, J. (2019). Examples of Project Cost Assumptions. azcentral. Retrieved from https://yourbusiness.azcentral.com/examples-project-cost-assumptions-28231.html
Quelch, J.A. & Rodriguez, M.L. (2015). Carolinas HealthCare System: Consumer Analytics. Harvard Business Review.
write