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There are many factors that determine the success or failure of a healthcare business or professional practice in
today's dynamic regulatory, reimbursement, and competitive environment. One of the most important of these factors is management’s ability to react to changes by making timely, informed decisions regarding the organization’s operational direction and financial performance. Among the most useful management tools available to achieve this objective is
benchmarking, a well established financial analysis process.
This article is presented in two parts. Part I, presented here, deals with sources of benchmarking data. Part II, presented in our November issue, will cover benchmarking a subject entity’s data against the industry.
Introduction
Benchmarking
can not only identify the existence of nonstandard performance and anomalies in costs, levels of productivity, and financial ratios, but
it can also help you discover their underlying causes. Once the driving factors for aberration from the norm are determined, they should be further investigated and assessed as to the potential weaknesses and risk factors, as well as the potential strengths they pose for the subject entity going forward. While this benchmarking process is essential for internal managers seeking to adjust business methods to optimize performance, it is also an invaluable tool for valuators and consultants.
The successful financial benchmarking analysis process may be divided into three
categories:
First, historical subject benchmarking compares the subject entity’s current or most recently reported performance with its past performance, a process that involves adjusting and comparing past data with current data. This method provides the basis for the most
accurate comparison by avoiding the complications of accounting/reporting differences that arise when comparing subject entity data with industry survey data. Historical subject benchmarking examines performance over time to identify changes in performance within a subject entity, to identify anomalies — such as extraordinary and non-recurring events — and to predict future performance.
Second,
benchmarking to industry norms compares subject entity data with survey data from other entities in the same industry sector and subsector. This method provides the basis for comparing the subject entity to similar entities to identify its relative strengths, weaknesses, and related risk measures.
As a required step precedent to these two benchmarking methods, the subject entity’s operating data is first “common sized.” In other words, it’s converted and expressed as a percentage or ratio of some measure, in one of several ways. Methods of common-sizing include expressing items on income and expense statements in terms of:
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Percentage of revenue or per unit produced — e.g., Relative Value Unit (RVU);
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Per provider — e.g., physician;
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Per capacity measurement — e.g., per square foot; or
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Other standard units of comparison.
A third type of benchmarking is
financial ratio analysis. These ratios are typically calculated as measurements of various financial and operational characteristics that illustrate the subject entity’s financial status. They’re evaluated in terms of their comparison to generally established industry norms expressed as ranges of positive or negative trends for that industry sector. A “current ratio” of less than 1.0, for example, might be considered “suspect.” When compared with ratios derived from survey data from the comparable industry sector, it may indicate that the subject entity’s resources are inadequate to meet its current obligations.
Healthcare industry survey benchmarking data may be obtained from several publicly available sources, enabling an analyst to compare detailed financial, operational, and clinical performance with similar peer group data.
The survey data should be as current as possible. Publication delays of a year or more are not uncommon,
so it’s easy to mismatch data from different years, diminishing the efficacy and applicability of the analysis to current and projected future operations. This is particularly true for medical practices because year-to-year changes can be significant and material in a rapidly changing reimbursement and regulatory environment.
I.
Sources of Benchmarking Data
There are a wide variety of published data sources for comparing the financial aspects of healthcare enterprises with the historical performance of industry peers on a national and regional scale. The following surveys represent some of the more widely accepted sources.
I.A. American Medical Association (AMA) Surveys
I.A.1. Physician Characteristics and Distribution in the U.S. (annual)
American Medical Association.
The AMA maintains the most comprehensive database of physician information in the U.S., with data on almost 800,000 MDs. Started in 1906, the AMA updates its “Physician Masterfile” annually through the Physicians’ Professional Activities questionnaire and the validation efforts of AMA’s Division of Survey and Data Resources. “Physician Characteristics and Distribution in the U.S.” is based on a variety of demographic information from this source. The database contains the largest sample of solo and small group practitioners.
I.A.2. Physician Socioeconomic Statistics
— AMA (CE) (annual)
American Medical Association (AMA).
This survey publication is the result of the merger in 2000 of the following two AMA annuals:
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Socioeconomic Characteristics of Medical Practice; and
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Physician Marketplace
Statistics
The merged survey is based on the AMA’s annual core survey of the Socioeconomic Monitoring System. Random samples of physicians from the Physician Masterfile were given a questionnaire and interviewed by telephone concerning a wide range of economic and practice characteristics. The annual publication reports data in the following categories:
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Age profiles of
physicians
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Weeks and hours of
practice
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Utilization of physician
services
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Fees for physician
visits
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Professional
expenses
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Physician
compensation
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Distribution of revenue by
payor
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Managed care
contracts and
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Other physician marketplace
statistics
I.B. Group Practice Associations Compensation and Production Surveys
Table 1 identifies the measures of revenue data provided in the various compensation and production surveys described in this section.
Table
1: Surveys Including Revenue Benchmarking Data
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Types
of Revenue Data
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AMA (CE)
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AMGA (C)
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MGMA (C)
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NAHC
|
|
1.
Accounts
receivable
|
|
|
X
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X
|
|
2.
Collections
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X
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X
|
X
|
X
|
|
3.
Compensation
|
X
|
X
|
X
|
X
|
|
4.
Gross
charges
|
|
X
|
X
|
X
|
I.B.1. Medical Group Compensation and
Productivity Survey — AMGA
(C) (annual)
American Medical Group Association
The American Medical Group Association (AMGA), formerly the American Group Practice Association, has conducted this compensation and production survey for
more than 17 years. Survey cosponsor McGladrey & Pullen surveys more than 2,600 group practices nationally. For the 2003 survey, 182 medical groups, representing almost 28,000 physicians, responded. Compensation and production data is provided for medical specialties by group size, geographic region, and single versus multi-specialty practices.
I.B.2. Physician Compensation and Production
Survey — MGMA (C) (annual)
Medical Group Management Association.
The Medical Group Management Association’s (MGMA) membership compensation and production survey has been conducted annually since 1987 and is one of the largest with approximately 1,800 practice respondents. Data is provided on compensation and production for 105 specialties with detailed summaries
of the 20 largest, including breakdowns for years in specialty, single or multi-specialty practice, geographic regions, and percentage of at-risk managed care revenues. Beginning with the 1997 report, the survey data is also published on CD by John Wiley & Sons ValueSource. The additional levels of detail available in this media provide better benchmarking capabilities.
Table
2: Compensation Criteria
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Compensation Criteria
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AMA (CE)
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AMGA (C)
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MGMA (C)
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NAHC
|
|
1.
Demographic
classification
|
|
|
X
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X
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|
2.
Employment
status
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X
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|
X
|
|
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3.
Gender
|
|
|
X
|
|
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4.
Geographic
section
|
X
|
X
|
X
|
|
|
5.
Group
type
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X
|
X
|
X
|
|
|
6.
Hours
worked per week
|
|
|
X
|
|
|
7.
Medical
specialty
|
X
|
X
|
X
|
X
|
|
8.
Method
of compensation
|
X
|
|
X
|
|
|
9.
Percentage
of capitation revenue
|
|
|
X
|
|
|
10.
Size
of practice
|
X
|
X
|
X
|
X
|
|
11.
Weeks
worked per year
|
|
|
X
|
|
|
12.
Years
in specialty
|
|
|
X
|
|
Table
3: Gross Charges Criteria
|
Gross
Charges Criteria
|
AMA (CE)
|
AMGA (C)
|
MGMA (C)
|
NAHC
|
|
1.
Employment
status
|
|
|
X
|
|
|
2.
Gender
|
|
|
X
|
|
|
3.
Geographic
section
|
X
|
|
X
|
X
|
|
4.
Group
type
|
|
|
X
|
X
|
|
5.
Hours
worked per week
|
X
|
|
X
|
|
|
6.
Medical
specialty
|
X
|
X
|
X
|
X
|
|
7.
Method
of compensation
|
|
|
X
|
|
|
8.
Percentage
of capitation revenue
|
|
|
X
|
|
|
9.
Size
of practice
|
X
|
|
X
|
|
|
10.
Weeks
worked per year
|
X
|
|
X
|
|
|
11.
Years
in specialty
|
|
|
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