Question:
A paper by R.N. Rodriguez (Health Care Applications of Statistical Process Control: Examples Using the SAS® System, SAS Users Group International: Proceedings of the 21st Annual Conference, 1996) illustrated several informative applications of control charts to the health care environment. One of these showed how a control chart was employed to analyze the number of office visits by health care plan members. The data for clinic E are shown in Table 7E.22. The variable NVISITE is the number of visits to clinic E each month, and MMSE is the number of members enrolled in the health care plan each month, in units of member months. DAYS is the number of days in each month. The variable NYRSE converts MMSE to units of thousand members per year, and is computed as follows: NYRSE = MMSE (Days/30)/12000. NYRSE represents the area of opportunity. The variable PHASE separates the data into two time periods.
The variable NYRSE can be thought of as an inspection unit, representing an identical area of opportunity for each sample. The process characteristic to be controlled is the rate of office visits. A u chart which monitors the average number of office visits per NYRSB is appropriate.
(a) Use the data from Phase 1 to construct a control chart for monitoring the rate of office visits performed at clinic E. Does this chart exhibit control?
(b) Plot the data from Phase 2 on the chart constructed in part (a). Is there a difference in the two phases?
(c) Consider only the Phase 2 data. Do these data exhibit control?
Transcribed Image Text:
TABLE 7E.22 Data for Evercise 7.80 Month Phase NVISITE NYRSE Days MMSE Jan. 94 I Fch. 94 Mar. 94 1 0 Apr. 94 1 1,576 0.64608 30 7,753 May 94 1567 0.66779 31 7,755 Jun. 94 1 July 94 1532 0.68105 3 7,909 Aug. 94 I Sep. 94 2 72 0.66717 30 8,006 Oct. 94 2 .762 0.9612 31 8,084 Nov. 94 21,853 0.68233 308,188 Dec. 94 1770 0.70809 31 8,223 Jan. 95 22,024 0.78215 3 9,083 Feh: 95 2 1975 0.70684 28 9,088 Mar 95 22097 0.78947 3 9.168 1421 0.66099 31 7.676 1303 0.59718 28 7.678 569 0.66219 3 7,690 1.450 0.65575 30 7,869 1,694 0.68820 31 7.992 2