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Man Versus Machine: Comparing Double Data Entry and Optical Mark Recognition for Processing CAHPS Survey Data
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Objective:
Historically, double data entry (DDE) has been considered the criterion standard for minimizing data entry errors. However, previous studies considered data entry alternatives through the limited lens of data accuracy. This study supplies information regarding data accuracy, operational efficiency, and cost for DDE and Optical Mark Recognition (OMR) for processing the Consumer Assessment of Healthcare Providers and Systems 5.0 survey.
Methods:
To assess data accuracy, we compared error rates for DDE and OMR by dividing the number of surveys that were arbitrated by the total number of surveys processed for each method. To assess operational efficiency, we tallied the cost of data entry for DDE and OMR after survey receipt. Costs were calculated on the basis of personnel, depreciation for capital equipment, and costs of noncapital equipment.
Results:
The cost savings attributed to this method were negated by the operational efficiency of OMR. There was a statistical significance between rates of arbitration between DDE and OMR; however, this statistical significance did not create a practical significance.
Conclusions:
The potential benefits of DDE in terms of data accuracy did not outweigh the operational efficiency and thereby financial savings of OMR.
Ovid Technologies (Wolters Kluwer Health)
Title: Man Versus Machine: Comparing Double Data Entry and Optical Mark Recognition for Processing CAHPS Survey Data
Description:
Objective:
Historically, double data entry (DDE) has been considered the criterion standard for minimizing data entry errors.
However, previous studies considered data entry alternatives through the limited lens of data accuracy.
This study supplies information regarding data accuracy, operational efficiency, and cost for DDE and Optical Mark Recognition (OMR) for processing the Consumer Assessment of Healthcare Providers and Systems 5.
0 survey.
Methods:
To assess data accuracy, we compared error rates for DDE and OMR by dividing the number of surveys that were arbitrated by the total number of surveys processed for each method.
To assess operational efficiency, we tallied the cost of data entry for DDE and OMR after survey receipt.
Costs were calculated on the basis of personnel, depreciation for capital equipment, and costs of noncapital equipment.
Results:
The cost savings attributed to this method were negated by the operational efficiency of OMR.
There was a statistical significance between rates of arbitration between DDE and OMR; however, this statistical significance did not create a practical significance.
Conclusions:
The potential benefits of DDE in terms of data accuracy did not outweigh the operational efficiency and thereby financial savings of OMR.
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