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Academia and the Profession |

Building Measurement and Data Collection into Medical Practice

Eugene C. Nelson, DSc, MPH; Mark E. Splaine, MD, MS; Paul B. Batalden, MD; and Stephen K. Plume, MD
[+] Article and Author Information

From Lahey Hitchcock Clinic and Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; Dartmouth Medical School, Hanover, New Hampshire; and Henry Ford Health System, Detroit, Michigan. Acknowledgments: The authors thank those who did the work summarized in the actual cases used in the paper: Drs. Diane Palac and Richard Whiting and nurses Brenda Moore, Joy Markelow, and Martha Coutermarsh from the Dartmouth-Hitchcock Medical Center general internal medicine urinary tract infection team, Lebanon, New Hampshire; the Central Vermont Hospital pneumonia team, Barre, Vermont; and Dr. Larry Staker, Salt Lake City, Utah. They also thank Marjorie Godfrey, MS, RN, who often served as the “spark plug,” and Diane Hall, who helped prepare the manuscript. Requests for Reprints: Eugene C. Nelson, DSc, MPH, Office of the President, Lahey Hitchcock Clinic, One Medical Center Drive, Lebanon, NH 03756-0001. Current Author Addresses: Drs. Nelson, Splaine, Batalden, and Plume: Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, NH 03756-0001.


Copyright ©2004 by the American College of Physicians


Ann Intern Med. 1998;128(6):460-466. doi:10.7326/0003-4819-128-6-199803150-00007
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Clinicians can use data to improve daily clinical practice.This paper offers eight principles for using data to support improvement in busy clinical settings: 1) seek usefulness, not perfection, in the measurement; 2) use a balanced set of process, outcome, and cost measures; 3) keep measurement simple [think big, but start small]; 4) use qualitative and quantitative data; 5) write down the operational definitions of measures; 6) measure small, representative samples; 7) build measurement into daily work; and 8) develop a measurement team.

The following approaches to using data for improvement are recommended. First, begin with curiosity about outcomes or a need to improve results. Second, try to avoid knee-jerk, obstructive criticism of proposed measurements. Instead, propose solutions that are practical, goal oriented, and good enough to start with. Third, gather baseline data on a small sample and check the findings. Fourth, try to change and improve the delivery process while gathering data. Fifth, plot results over time and analyze them by using a control chart or other graphical method. Sixth, refine your understanding of variation in processes and outcomes by dividing patients into clinically homogeneous subgroups (stratification) and analyzing the results separately for each subgroup. Finally, make further changes while measuring key outcomes over time.

Measurement and improvement are intertwined; it is impossible to make improvements without measurement.Measuring and learning from each patient and using the information gleaned to test improvements can become part of daily medical practice in local settings.

Figures

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Figure 1.
Statistical process control chart of the fasting blood glucose level of a patient with type 2 diabetes on consecutive days.[16]

The solid line represents the mean serum glucose level plotted over time. The dotted lines represent the upper and lower control limits or natural process limits for the measured variable. The arrow indicates the point at which patients began self-monitoring their blood glucose levels. The upper and lower natural process limits were computed by using the following formula: mean ± 2.66 (average point-to-point variation, also called the moving range). This formula is recommended by Wheeler and Chambers for calculation of process limits when the size of the subgroup is 1; it was used because each data point is a measurement from a single patient. Calculations and graphics were done with Microsoft Excel, version 4.0 (Redmond, Washington). No techniques were used to smooth data. Actual values for days 11 to 24 were as follows: mean blood glucose level, 135 mg/dL; upper natural process limit, 171 mg/dL; lower natural process limit, 98 mg/dL; and moving range, 14 mg/dL. To convert mg/dL to mmol/L, multiply by 0.05551.

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Figure 2.
Instrument panel of three statistical process control charts for hospitalized patients with community-acquired pneumonia.[16]

Duration of intravenous antibiotic therapy, time to administration of antibiotic therapy, and average length of hospital stay were thought to be key measures that the pneumonia care team wanted to follow over time. The solid lines represent the mean values plotted over time. The dotted lines represent the upper and lower control limits or natural process limits for the measured variables (lower limits in the top and middle panels were less than zero and are not shown). The arrows indicate the points at which changes were implemented. The upper and lower natural process limits were computed by using the following formula: mean ± 2.66 (average point-to-point variation, also called the moving range). This formula is recommended by Wheeler and Chambers for calculation of process limits when the size of the subgroup is 1; it was chosen because each data point is a measurement from a single patient. Calculations and graphics were done with Microsoft Excel, version 4.0 (Redmond, Washington). No techniques were used to smooth data.

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