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Merit Medical Billing Associates LLC is a medical practice revenue cycle management firm. The company assists physicians with issues and problems that directly impact productivity and cash flow such as managing day-to-day operations, patient registration, from insurance verification to billing clean claims, to marketing to increase their patient base. Merit Medical Billing services go beyond standard measures to analytically track critically important billing and reimbursement problems to ensure physicians receive the reimbursement they work so hard to obtain. The company’s consultative approach is designed to address everything from major strategic issues to more basic problems affecting everyday business operations.
Merit Medical Billing Associates (MMBA) is faced with the task of assisting a large medical practice of about twenty-five physicians estimate their potential revenue from the patients in the surrounding area. The physicians also wanted to gather benchmark values that effected the practice such as patient care margins which would feed into patient satisfaction. While patient care and patient satisfaction are qualitative methods, for the specifics pertaining to revenue reporting, quantitative methods would be better suited for this purpose. This practice treats about two thousand twenty-five patients per month that is an average of approximately twenty patients per week per physician. With the growing population in the surrounding area as well as the changing dynamic surrounding insurance attainability the physicians in this practice need and want to know where the majority of the revenue is coming from and the proactive sustainability measures they may need to take.
In addition, with the implementation of Value-based payment implemented by the Centers for Medicare & Medicaid Services (CMS), physician practices must take a more analytic approach to improve performance across the board. With the Value-base payment, physicians would only be made eligible for reimbursement for services rendered, if and when the physicians adhere to and align with quality measures set by the Centers for Medicare and Medicare Services, (CMS.gov 2016). One research variable would be the patients with insurance, whether it is commercial insurance such as Blue Cross Blue Shield or United Healthcare or government insurance such as Medicare and Medicaid. This could actually be considered two variables; patients with commercial insurance versus patients with government insurance however, for this data research the population with insurance will be the variable.
To collect suitable sample quantitative data about two hundred patient registration and medical records, including new and established patients, will be pulled and reviewed for insurance. The records will be reviewed and insurance benefits re-verified to confirm coverage as well as coverage limitations. Since insurance payer’s requirements for authorization/precertification may change with the type of service or treatment a patient needs, authorization requirements must also be verified. A physician’s failure to obtain proper authorization/precertification will result in claim denial and thus negatively affects revenue. Because practice office staff must concentrate on patient care and accurate data entry, the consultants at Merit Medical Billing Associates will take on the task to insure insurance benefit verification and authorization requirements are validated.
Analyzing the Data
Once the sample data is collected we then choose a level of measurement in which to analyze the data whether it is nominal, ordinal, interval, or ratio. According to Centers for Innovation in Research & Teaching (2016), “The first step in quantitative data analysis is to identify the levels or scales of measurement”. This analysis can be a simple and basic measurement of nominal data where the values are categorized as insured patients and uninsured patients. We will know if the data collection method used would generate valid and reliable data by using a meaningful median to calculate the given ordinal data. However to get a comprehensive view of the data collection it would be best to construct a frequency and percent distribution tabulation of the variable. This would require breaking down the variable in further, aside from insured and uninsured patients, taking into consideration gender (how much of the sample population are male and how much female); ethnicity (how much of the sample population African American, White, or Latina); and employment status. Some physicians rely on their consultants to provide them with solutions to issues affecting their business growth and sustainability. Organizing the data in this manner will allow the production of a comprehensive easy to understand reports including charts for the physicians.