Memorial Health System Case Study

experience.

The other key characteristic that Memorial Health System is deficient in is enterprise-wide analytics. Davenport and Harris state that enterprise-level management is ensuring and data and analyses are made available throughout an organization to ensure proper care is taken to manage this information effectively (Davenport & Harris, 2007, p. 27) . They also go on to explain that you should not make business decisions on narrow, or incorrect data, or it could result in serious consequences (Davenport & Harris, 2007, p. 27).

At MHS, the intent was never to purchase products that would lead to incorrect data being used to make decisions, but that is exactly what has happened in the past. The administrative team heavily relied upon The Advisory Board, a consulting company, products to make decisions on patient trends and spend. However the data the Advisory Board was reporting was incomplete and never verified resulting in incorrect spend and reimbursement projections. The validity of the data was challenged by the decision support team when trying to link data to another, reliable, data source. There is still a large focus on the products of the

Advisory Board, which could lead to the demise of the health system remaining independent.
Davenport and Harris (2007) believe that to assess the four key characteristics, there are different stages that report how much or how little a company is competing on analytics. The stages range from analytically impaired, stage one, to full blown analytical competitors, stage five (Davenport & Harris, 2007, pp. 34-35). Based off of these stages, MHS would be classified in stage two or three.
Stage one, or analytically impaired is described as companies that are making decisions based off of intuition, rather than data and analytics (Davenport & Harris, 2007, p. 34). At one time all businesses were using this technique, feeling as if there were not enough resources, or technological advances, to make good business decisions. The lack of technology resulted in using past scenarios and intuition to make decisions for the health system. Once the company began implementing electronic record keeping, it became easier to gather data through IT and standard system reports. The data was not extensively analyzed as it is now, but it assisted in making educated decisions. Now that the health system is fully functioning on an electronic health record system reporting is a necessity for day to day functions.
Stage two, or localized analytics, are companies that are trying to “use analytics to improve upon one or more activities” (Davenport & Harris, 2007, p. 36). This stage is one that MHS can closely relate to. The administrative team, as well as department managers, are starting to question processes. Furthermore they are interested in finding more efficient ways to complete routine tasks. The health system has implemented a LEAN department where members have achieved green to black belt certification through Lean Six Sigma programs. Along with LEAN and process mapping, the information that is being provided by the decision support team is assisting in tracking productivity for the health system.
The productivity module has been very successful in keeping costs at a minimum while functioning safely and efficiently. Manageable targets were created for each individual department based on the specific functions performed by the department. Some of these indicators include staffing matrixes and variable patient units (i.e. adjusted discharges, billable procedures, etc.). These key indicators were compared to a national database to create competitive targets that each department is expected to achieve. Even with the success of the program, the information is all recorded in Excel spreadsheets using multiple VLOOKUP formulas and various calculations. Information is manually entered by a member of the decision support team from multiple sources, which leaves a large margin for error. Along with this margin, the appearance of the program is not aesthetically pleasing.
Stage three, or analytical aspirations, are companies that are making efforts to become more analytical to improve distinct capabilities (Davenport & Harris, 2007, p.

36). Memorial Health System has been trying to utilize data to make good business decisions for some time now however the lack of knowledge on how to conquer data was the most challenging aspect. Now that senior management is supportive of moving toward data and analytical based decisions, the company is powering to the third stage as quickly as possible. Through creating a new department, hiring individuals, and purchasing analytical software, it appears that Memorial Health System is making great strides to be more analytically competitive. The decision support team has been tasked with most of the heavy lifting into the stage of

analytics.
The decision support team has helped to review the effects of Sepsis, a life threatening diagnosis if left untreated, on the community. Currently the health system relies mostly on symptomatic responses and diagnostic testing to confirm the presences of septicemia, both of which are not the most effective measure. The team conducted a data driven analysis to see how many patients were diagnosed with septicemia in a specific period of time and how the diagnosis was concluded. The results determined that the best business decision would be for the health system to purchase a piece of laboratory equipment that would confirm diagnosis off of a simple blood test. Even though the piece of equipment was an initial investment, the cost of inpatient visits would be dramatically reduced, resulting in a long term return on investment.