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Modeling Healthcare Processes Using Commitments: An Empirical Evaluation.

Telang PR, Kalia AK, Singh MP - PLoS ONE (2015)

Bottom Line: Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student's t-test to the 10% level of significance.Comma is a promising new approach for modeling healthcare processes.Further gains could be made through improved tooling and enhanced training of modeling personnel.

View Article: PubMed Central - PubMed

Affiliation: Cisco Systems Inc., Research Triangle Park, Durham, North Carolina, United States of America.

ABSTRACT
The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7-each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student's t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel.

No MeSH data available.


Related in: MedlinePlus

Distributions of quality, as judged by experts.
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pone.0141202.g006: Distributions of quality, as judged by experts.

Mentions: Fig 6 shows the subjective quality of the SDs judged independently by two experts. (The experts reconciled any differences through discussions.) Fig 6(a) shows the scenario coverage for the SDs. Observe that the scenario coverage is high for both Traditional-HL7 (92%) and Comma (92%), and for the modification task, the scenario coverage for CommaM (82%) is slightly lower than Traditional-HL7M (88%). The higher scenario coverage in both the approaches may be due to the scenario being small. Fig 6(b) shows the precision for the SDs. Observe that the Comma and CommaM precision (40% and 61%, respectively) is higher than that of Traditional-HL7 and Traditional-HL7M (18% and 40%, respectively). We attribute Comma’s higher precision to its systematic nature and the fact that it focuses attention on the relevant commitments. Fig 6(c) shows the comprehensibility of the models. The comprehensibility for Comma (32%) is higher than Traditional-HL7 (14%), which we attribute to Comma’s modular patterns. Further, observe that the comprehensibility for Traditional-HL7M (92%) is slightly higher than that of CommaM (89%).


Modeling Healthcare Processes Using Commitments: An Empirical Evaluation.

Telang PR, Kalia AK, Singh MP - PLoS ONE (2015)

Distributions of quality, as judged by experts.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4634947&req=5

pone.0141202.g006: Distributions of quality, as judged by experts.
Mentions: Fig 6 shows the subjective quality of the SDs judged independently by two experts. (The experts reconciled any differences through discussions.) Fig 6(a) shows the scenario coverage for the SDs. Observe that the scenario coverage is high for both Traditional-HL7 (92%) and Comma (92%), and for the modification task, the scenario coverage for CommaM (82%) is slightly lower than Traditional-HL7M (88%). The higher scenario coverage in both the approaches may be due to the scenario being small. Fig 6(b) shows the precision for the SDs. Observe that the Comma and CommaM precision (40% and 61%, respectively) is higher than that of Traditional-HL7 and Traditional-HL7M (18% and 40%, respectively). We attribute Comma’s higher precision to its systematic nature and the fact that it focuses attention on the relevant commitments. Fig 6(c) shows the comprehensibility of the models. The comprehensibility for Comma (32%) is higher than Traditional-HL7 (14%), which we attribute to Comma’s modular patterns. Further, observe that the comprehensibility for Traditional-HL7M (92%) is slightly higher than that of CommaM (89%).

Bottom Line: Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student's t-test to the 10% level of significance.Comma is a promising new approach for modeling healthcare processes.Further gains could be made through improved tooling and enhanced training of modeling personnel.

View Article: PubMed Central - PubMed

Affiliation: Cisco Systems Inc., Research Triangle Park, Durham, North Carolina, United States of America.

ABSTRACT
The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7-each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student's t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel.

No MeSH data available.


Related in: MedlinePlus