Recent Announcements


BC Medical Insurance Billing Module Version 2.0 is released

posted Apr 7, 2011, 8:00 AM by Post Master

The OSCAR Vollies have released the version 2.0 of BC Billing module! this is a UI to rules and regulations announced by the government of British Columbia here:


A demo version is available here:
 
 https://rivercityclinic.net:13242/bcbilling/

and the project website can be found over here:
http://oscarbcbilling.patesco.ca/

Way to go OSCAR Volunteers! Patesco is proud to be associated with this great team and project. We look forward to witnessing your continued success and accomplishments! 

The Data Quality Management Framework is released

posted Oct 14, 2009, 11:16 PM by Post Master   [ updated Apr 7, 2011, 7:55 AM ]

In the present era, data is one of the most important factors for business survival of any industry, and healthcare is no exception. In fact, the drive to reduce cost and the desire to improve patient care makes the healthcare industry one of the most eager customers for clean and integrated organizational data.

 But this data needs to posses a number of characteristics if it is to be used for decision-making and strategic operation. The government of British Columbia defines the state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use as data quality

Rounded Rectangle: Data Consumers: Use the data for planning, outcome assessments and decisionsRounded Rectangle: Data Custodians: Code, abstract, verify, validate, aggregate and maintain the data; circulate reports to usersRounded Rectangle: Data Producers: Provide Data from Inside or Outside of the organizationTo build a practical framework for achieving desirable data quality, Strong et al. (2) suggest taking a customer focused view by treating data processing as a data manufacturing system and to produce data that is “fit to use by data customer”. They recognize three fundamental roles: data producers, data custodians and data consumers. Fitness for use would mean that the concept of data quality is relative; therefore dimensions need to be defined to ensure fitness in an integrated health care environment. 

Table 1: Perceived Data Quality Dimensions

Information Quality Category

Information Quality Dimensions

Intrinsic

Accuracy, Objectivity, Believability, Reputation

Accessibility

Access, Security

Contextual

Value-added, Relevancy, Timeliness, Completeness, Amount of Data

Representational

Interpretability, Ease of Understanding, Concise Representation, Consistent Representation

Organizations select from these dimensions based on their own needs and special circumstances. Examples include New Zealand Healthcare data quality framework (6 dimensions), Statistics Canada’s Quality Assurance Framework  (6 dimensions), Ontario Data Quality Management Framework (4 dimensions) , and CIHI Data Quality Framework, which will be presented here in further detail.

CIHI has been publishing a framework for data quality since 2000. the latest revision, 2006, has selected five of these dimensions to implement a data quality assessment tools (10).

CIHI selects Accuracy, Timeliness, Comparability, Usability, and Relevance. They then divide these dimensions into characteristics, and define a set of criteria for each characteristic. Using a ranking system of met, unmet, and unkown or not applicable, the strengths and limitations of data is assessed, and areas of intervention identified. To help prioritize those intervention activities, some of the criteria follow another ranking system of minimal, moderate and significant.

 CIHI defines the roles and responsibilities of data quality framework as follows:

  • Senior Management
    • Provide Resource, Support DQ in all new initiative
  • Product Areas
    • Evaluate data quality and address issues
    • Document quality
    • Conduct studies and identify ways of improvement
  • Data Quality Section
    • Provide guidance, review and report on compliance
    • Assist in studies
    • Conduct R&D on data quality
    • Train and update framework annually

 

 Full text article is available in the Resources section.

Lean Six Sigma in HealthCare

posted Oct 14, 2009, 11:11 PM by Post Master   [ updated Apr 7, 2011, 8:00 AM ]

Process modeling measures, analyses, improves and controls characteristics of the business process in an organization. It demands the commitment of everyone in the organization, particularly high-level management, to achieve sustainable quality improvement. It reasons that business success depends on reduction of process variation, with continuous effort to establish stable and predictable process results.

The historical success of organizations that implemented correct process modeling has made this topic a common practice for all organizations that wish to optimize their performance; and with increasing demand on health care information and limited financial resources, health care organizations have followed suite of the major industries and are now increasingly implementing these concepts.

Six Sigma, christened after the statistical process capability studies, was originally a set of practices to improve manufacturing process developed by Bill Smith at Motorola in 1986. However, it has expanded in the two subsequent decades to extend to other types of business processes as one of the most popular quality improvement strategies.









The paper is divided to two main parts. The first part presents a review of  current literature, showing the facts and issues of each concept. It introduces the Six Sigma strategy and the Lean Manufacturing approach, and describes how the combination of these, Lean Six Sigma, is used as a methodology for business process management.

 The SIPOC diagrams are then described in further detail, pointing out how should each part (Supplier, Input, Process, Output, and Customer) should be documented and described. The Swim lane diagrams that depict the processes are then introduced and their expected layout and properties discussed.

 Once the processes are defined correctly by the SIPOC diagrams, they need to be analysed. Value Stream Mapping and Quality Stream Mapping are two of these tools that are described here as the last section of the first part of this document.

 The second part defines the metadata for SIPOC diagrams. Here, tables demonstrate each data element that should be recorded about each part of the system, and an example or a description is presented to help specify that element correctly. A set of graphic icons is represented at the end of the document to be used in process maps so that they would all follow a uniform visual presentation.


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