Limits...
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

A Traditional-HL7 solution for the ASPE scenario.(a) patient requests physician to perform diagnosis.(b) physician requests radiologist to perform imaging.(c) physician requests radiologist to perform biopsy.(d) radiologist requests pathologist to provide pathology report.(e) pathologist registers patient with registrar.(f) radiologist sends radiology-pathology integrated report to physician.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4634947&req=5

pone.0141202.g002: A Traditional-HL7 solution for the ASPE scenario.(a) patient requests physician to perform diagnosis.(b) physician requests radiologist to perform imaging.(c) physician requests radiologist to perform biopsy.(d) radiologist requests pathologist to provide pathology report.(e) pathologist registers patient with registrar.(f) radiologist sends radiology-pathology integrated report to physician.

Mentions: Table 3 and Fig 2 respectively show the messages and sequence diagrams of a Traditional-HL7 solution to the breast cancer scenario. In Fig 2(a), patient requests physician for a checkup by sending a patient problem (PPR) message, and physician sends an acknowledge (ACK) message back to patient. In Fig 2(b), after receiving the patient problem, physician requests radiologist for imaging by sending a general order (ORM) message. radiologist delivers the imaging report by sending a general order response (ORR) message. In Fig 2(c), based on the imaging report, if physician finds the tumor suspicious, physician asks radiologist to perform a biopsy, via an order biopsy (ORM) message. In Fig 2(d), after sending order biopsy, radiologist requests pathologist to examine patient’s tissue specimen via order path report (ORM) message. pathologist submits his report via pathology report (ORR) message. In Fig 2(e), after sending pathology report, pathologist informs registrar about the patient via register patient (ORU) message, and upon registering, registrar sends patient registered (ACK) message to pathologist. Finally, in Fig 2(f), radiologist provides physician with patient’s biopsy report via radpath biopsy (ORR) message.


Modeling Healthcare Processes Using Commitments: An Empirical Evaluation.

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

A Traditional-HL7 solution for the ASPE scenario.(a) patient requests physician to perform diagnosis.(b) physician requests radiologist to perform imaging.(c) physician requests radiologist to perform biopsy.(d) radiologist requests pathologist to provide pathology report.(e) pathologist registers patient with registrar.(f) radiologist sends radiology-pathology integrated report to physician.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0141202.g002: A Traditional-HL7 solution for the ASPE scenario.(a) patient requests physician to perform diagnosis.(b) physician requests radiologist to perform imaging.(c) physician requests radiologist to perform biopsy.(d) radiologist requests pathologist to provide pathology report.(e) pathologist registers patient with registrar.(f) radiologist sends radiology-pathology integrated report to physician.
Mentions: Table 3 and Fig 2 respectively show the messages and sequence diagrams of a Traditional-HL7 solution to the breast cancer scenario. In Fig 2(a), patient requests physician for a checkup by sending a patient problem (PPR) message, and physician sends an acknowledge (ACK) message back to patient. In Fig 2(b), after receiving the patient problem, physician requests radiologist for imaging by sending a general order (ORM) message. radiologist delivers the imaging report by sending a general order response (ORR) message. In Fig 2(c), based on the imaging report, if physician finds the tumor suspicious, physician asks radiologist to perform a biopsy, via an order biopsy (ORM) message. In Fig 2(d), after sending order biopsy, radiologist requests pathologist to examine patient’s tissue specimen via order path report (ORM) message. pathologist submits his report via pathology report (ORR) message. In Fig 2(e), after sending pathology report, pathologist informs registrar about the patient via register patient (ORU) message, and upon registering, registrar sends patient registered (ACK) message to pathologist. Finally, in Fig 2(f), radiologist provides physician with patient’s biopsy report via radpath biopsy (ORR) message.

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