Case Scenario:
Patient Service Clinics, are medical facilities where sick people go in order to receive standard medical care and are minimally equipped with essential laboratory and X-ray instruments and are staffed by only one primary care provider, in the form of either a Nurse Practitioner or Physician Assistant; working under the supervision of a physician, within an established radius. Patient Service Clinics that don’t achieve favorable productivity levels or positive contribution margins are targeted for financial and production scrutiny, and even closure. The results of this study will help provide you with a good understanding and measuring stick to decide whether a certain patient service clinic should be kept open or closed - based on its profitability, production levels and or strategic location.
Define the Problem:
In order to have a better understanding and assess the probability of closing a patient service clinic, one must first decide if the clinic meets the business criteria for profitability, productivity and strategic location.
Identify the Criteria:
Part of the process of making a rational and well informed decision involves recognizing all of the crucial factors that will assist in decision making (Bazerman & Moore, 2009). Successful execution of this project will depend primarily on the author’s ability to find the criteria around profitability, productivity and strategic location and implementation of this project will require the numerous departments to work together. Information has to be shared in a system that will be well-matched with all areas concerned and in a way that will meet the needs of the author. Profitability is a financial metric that measures a business ability to generate earnings in relation to its expenses and other costs incurred during a specific period of time). Our company uses Contribution Margin Percent (CM%) as an marker of profitability.
Revenues – Total Operating Expenses / Revenues X 100 = Contribution Margin Percent.
The company has set some general rules when it comes to the interpretation of profitability, but lacks a proper method to efficiently track the necessary information to make these kinds of decisions and the same problem occurs with the company’s interpretation of productivity as well. Ralphs (1999) defines productivity as the change between the cost and value of a unit of work. In the clinical setting this translates to the number of patients seen per full time employee, per revenue day and this information is created and assessed by various departments that are not interfaced and therefore quick recovery and evaluation of this data is very complex. These inefficiencies produce deep problems when attempting to determine the feasibility of a patient service clinic.
Weigh the Criteria:
A rational decision maker will be well informed, and will know the value of each identified criteria and therefore it is important that a value system be assigned. The most important problem is to verify the legitimacy of the financial data. The next step we want to do is to make sure that production levels information is accurate, and reflective of actual “field” information. The number of full time employees in the clinic should reflect the number of employees in the general ledger. There was an understanding that this data might be skewed, since some of the clinics accept students that train and do some work, but don’t get paid, thus affecting functional productivity and furthermore there are Floaters that would cover during vacation and extended periods of leave.
Finally, it was decided that location would play a lesser role in the decision making process, since there might be occurrences where more than one patient service clinic would be present in the area being assessed but still had to be measured because some of the clinics were placed based on insurance provider demands.
Generate Alternatives:
The next course of action was to find the possible course of action (Bazerman &, 2009). The challenge was to not spend too much time on the alternative method. Spending too much time on the alternative would make the decision harder but in this specific case there were only two alternatives: to keep the clinic open or to close it.
Rate Each Alternative on Each Criterion:
This was the most difficult step and the finance department was given the task of forecasting and the operations team had to predict production levels based on the financial forecast. Assessing the future profitability of this clinic included the examination of many variables like: financial trends, population, participating insurance providers, health care providers and voice of the customer (via surveys). Every area was scrutinized and the potential consequences were examined.
Compute the Optimal Decision:
After completion of the previous steps, the data was then assessed and weighted. The final results revealed that we should have closed the clinic, as a result of low productivity and its poor location, however the final decision made was to keep the clinic open. Once the problem was indentified (to keep the clinic open or to close it); the convincing criteria (location, productivity and profitability) were identified and weighted and then pick from the two alternatives and the decision to keep the clinic open was based on results from financial forecasts. The biggest problem was giving a numerical value to the important but yet subjective criterion of location and thus there was no consensus as to its numerical value. The biggest weakness that was found with the rational model of decision making was its inability to account for personal preferences and also the model’s has an inability to account for our moral and ethical preferences, which can interfere with our judgment.
References:
Investopedia. (2009) What Does Profit Mean? Retrieved March 28, 2011, from
https://www.investopedia.com/terms/p/profitabilityratios.asp
Stephen Ralphs. (1999, December). Defining productivity is a must for all organizations. Computing Canada, 25(47), 9. Retrieved March 28, 2011, from ABI/INFORM Global. (Document ID: 47486482).
Chen-Bo Zhong. (2008).The ethical dangers of rational decision making. Academy of Management Proceedings, p1-6,6p. Retrieved March 29, 2011 from Business Source Premier database.
Bazerman, M.H., & Moore, D.A. (2009). Judgment in managerial decision making (7th ed.). Hoboken, NJ: Wiley