PnP DSS

Pregnancy & Parturition Process DSS

Himanshu ChavdaLemuel DizonAteeq Jaffer

March 5th, 2009

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IntroductionVBAC - Vaginal Birth After Cesarean.

1996 28% VBAC

Healthy People 2010 Target VBAC 37%

2006 8% VBAC

ACOG – American College of Obstetricians and Gynecologists.

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“Once a Cesarean, always a Cesarean”

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Facts about Cesarean

Cesarean delivery rate (CDR) in the US in 1970 was 5.5 per 100 delivery and increased to 24.7 in 1988. By 2007 the rate has increased to 33% .U.S. health objective to reduce CDR to 15%World Health Organization (WHO) recommends 5 –10%. Above 15% seem to do more harm than good.

Cause of Increasing CDR

• Low priority of enhancing women's own abilities to give birth.

• Side effects of common labor interventions.

• Refusal to offer the informed choice of vaginal birth.

• Casual attitudes about surgery and cesarean sections in particular.

• Limited awareness of harms that are more likely with cesarean section.

• Providers' fears of malpractice claims and lawsuits.

• Incentives to practice in a manner that is efficient for providers.

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PnP DSS Overview

MODEL Decision Tree Knowledge Engineering

SYSTEM Architectural Diagram Technology User Interface

EVALUATION Sensitivity Analysis Pilot Programs

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PnP Model DesignThis CDSS will allow clinicians and expected mother on the informed choice of delivery mode.Decision tree models various studies for uncertainties in terms of probabilities, values/utilities and attributes. The terminal values are utility scores from a certain study that was attributed to a natural ranking between 0 & 1. A better outcome meant a higher score. The core logic utilize Bayesian inference to compute the probability of each uncertainty from [1,2] research studies.The model analyzes some the common pregnancy uncertainties such as:

The core objective is to help expected mothers with history of prior CS to reach a best possible decision during the pregnancy.

Maternal DeathCerebral PalsyStill BirthHysterectomy

PregnancyNo PregnancyRescue CSNormal delivery

MorbidityHealthy BirthMortality/Death

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PnP Model – Decision Tree

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Knowledge EngineeringTreeAge Software Ver. 3.5Published study by Sadan et al in Arch Gynecol Obstet. Published study by Vandenbussche et al in BIRTH.TOL Vs Repeat CS.

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PnP System Design Architectural Diagram

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PnP System Design - Technology

Method A – Using the PnP Website to Query the data directly from PnP DSS stationed at the Vendor Site

The PnP DSS will have an interactive website which the authorized personnel can access for their needs. The login page on the PnP website would ask for the user credentials. The Patient registration form on the PnP website should have the following Fields to capture pertinent data (Patient data, Medical History, Comments) The clinician registration form on the PnP website should have the following Fields to capture pertinent data (Clinician data, Area of Expertise, CSI #, Comments)The application will be driven using Apache Tomcat, which will implement the servlets and JSP or struts (framework)

Here’s an excerpt of the Data Interchange process between PnP Website & PnP DSS The PnP application server captures the data submitted from the PnP website by the end-users, and will communicate the data request to the web-server in the PnP DSS. The data will be transmitted via HTTP/HTTPS protocol. The PnP DSS system will query its database with the pertinent data (requested thru the website) & render it back to the PnP website.

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PnP System Design - Technology

Method B – Using the PnP DSS driven by feeds from PnP ODS (Operational Data Store) In this option, the PnP DSS residing at the Clinical Institution gets a feed from the PnP ODS on a predetermined basis. The PnP DSS is utilized to serve the needs of the patients & Clinicians in making an informed & educated decision. The PnP ODS will serve as a Knowledge repository which contains the data pertaining to PnP process as well as a ‘Data Collection & Analysis’ repository to acquire the latest information pertaining to the PnP process & reinforce the Knowledge respository with the updated/refined information.

Here’s an excerpt of the Ongoing Data Interchange process between PnP DSS & PnP ODSThe PnP application server from the PnP DSS will communicate the data to the webserver in the PnP ODS. The data will be transmitted via HTTP/HTTPS protocol. The PnP ODS system will update its database with the pertinent data (Delta, Changes) & incorporate it in the program logicOnce the validity of the new findings is confirmed, the PnP ODS application server will communicate that to the PnP DSS webserver.The PnP DSS can now be queried for the latest information pertaining to the PnP process.

Here’s an excerpt of the Data retrieval process between PnP Website & PnP DSS The PnP application server captures the data submitted will communicate the data request to the webserver in the PnP DSS. The PnP DSS system will query its database with the pertinent data (requested thru the website) & render it back to the PnP website.

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PnP System Design - Technology

Method B – Using the PnP DSS driven by feeds from PnP ODS (Operational Data Store)

Data Interchange Options

Option 1 - Web services can be employed to effect data Interchange between PnP DSS & PnP ODS

Web services are Web based applications that use open, XML-based standards and transport protocols to exchange data with clients.

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PnP System Design - Technology

Method B – Using the PnP DSS driven by feeds from PnP ODS (Operational Data Store)

Data Interchange Options

Option 2 – Data Interchange between PnP DSS & PnP ODS via HTTP/S Forms

Data can be interchanged between PnP DSS & PnP ODS through the respective application servers using the http/https protocol.

This is kind of a tight coupling.

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User Interface

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Implementation & EvaluationSensitivity AnalysisPilot Programs

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Feedback: Expectation and Knowledge Scale

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PnP DSS Product Evaluation

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References :1. Sadan, O., Leshno, M., Gottreich, A., Golan, A., Lurie, S., “Once a cesarean always a cesarean? A computer-assisted

decision analysis.” Arch Gynecol Obstet (2007) 276:517–521. PubMed. Gulter Health Sciences Library, Chicago. 2 Feb. 2009. http://www.galter.northwestern.edu/.

2. Frank P.H.A. Vandenbussche, MD, Lieke C. De Jong-Potjer, MD, Anne M. Stiggelbout, PhD, Saskia Le Cessie, PhD, and Marc J.N.C. Keirse, MD, DPhil, FRACOG,. FRCOG, “Differences in the Valuation of Birth Outcomes Among Pregnant Women, Mothers, and Obstetricians.” BIRTH 26:3 September 1999. PubMed. Gulter Health Sciences Library, Chicago. 2 Feb. 2009. http://www.galter.northwestern.edu/.

3. Caroline Signore, MD, MPH, Anusha Hemachandra, MD, MPH, and Mark Klebanoff, MD, MPH., “Neonatal Mortality and Morbidity After Elective Cesarean Delivery Versus Routine Expectant Management: A Decision Analysis.” Seminars in Perinatology 2006.07.010. PubMed. Gulter Health Sciences Library, Chicago. 4 Feb. 2009.

4. Naomi E. Stotland, MD,a,b Lisa S. Lipschitz, MD,a and Aaron B. Caughey, MD, MPP, MPHa., “Delivery strategies for women with a previous classic cesarean delivery: A decision analysis.” 2002, Mosby, Inc. mob.2002.127123. PubMed. Gulter Health Sciences Library, Chicago. 4 Feb. 2009.

5. Cynthia J. Sims, MD,a Leslie Meyn, BS,a Rich Caruana, PhD,b, c R. Bharat Rao, PhD,d Tom Mitchell, PhD,b and Marijane Krohn, PhDa. “Predicting cesarean delivery with decision tree models.” 2000, Mosby, Inc. mob.2000.108891. PubMed. Gulter Health Sciences Library, Chicago. 4 Feb. 2009.

6. Alan A Montgomery, Clare L Emmett, Tom Fahey, Claire Jones, Ian Ricketts,Roshni R Patel, Tim J Peters, Deirdre J Murphy and DiAMOND Study Group. “Two decision aids for mode of delivery among women with previous caesarean section: randomised controlled trial.” BMJ, doi:10.1136/bmj.3921 7.67101955 (published 04 April 2007). PubMed. Gulter Health Sciences Library, Chicago. 31 Jan. 2009.

7. http://www.time.com/time/magazine/article/0,9171,1880665,00.html

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