Electronic Medical Billing Knowledge Base With Communal Document Control + Adaptive Search Software

Google became a standard reference tool for almost every group of age and interest. But Google frustrates doctors looking for better understanding of medical billing complexities and modern straight through billing technologies. This article briefly explores the contradictory forces of the most popular search algorithm on the planet and outlines specialized, collaborative, and self-learning technologies to solve its limitations in the context of medical billing and compliance.

Google\'s Limitations

As of the end of January 2005, the indexable Web was estimated at 11.5 billion pages [Gulli and Signorini], growing from 200 million pages in 1997. Without effective search engines, such as Google, the Web would either not achieve such huge proportions in such a short time or become the epitome of document anarchy. Internet search engines in general and Google in particular have transformed the World Wide Web from stagnant data repository with a slow and result-poor search process to a dynamic world of continuously expanding and rich source of information of gargantuan proportions.

Google today represents state of the art of \"group mind,\" which is defined by people who actively create and manage text on the Internet and hyperlinks to that text. Its success is rooted in relevance of retrieved results and in high speed of result retrieval. Google proved that using page popularity to rank its relevance to the original query has tremendous pragmatic value to the broadest groups of Internet users.

However, the same popularity-based search algorithms also cause imperfections and frustrations. Google superimposes only minimal order to this anarchy, just enough to find tons of interesting information but far from serving a comprehensive reference source. It would be a mistake to rely on Google for an ultimate source of reference information [Hargittai]. The list of Google\'s shortcomings includes

1. Low precision. Google does not return an answer or solution to your problem. Instead, you receive a set of links to Web pages with information of various degrees of relevance to your search. This kind of answer results in multi-step operation, where you first must identify the most promising links, then \"drill in\" for more information, refine your initial search and repeat the entire process. For instance, a search for \"medical billing\" returns ads for medical jobs, career guides, online learning courses, and advertisements for billing products.
2. No context. For example, a search for \"medical compliance,\" retrieves advertisement to hire reimbursement compliance consultants along with a source for the top quality disposables, and government employment regulations for leave of absence due to medical necessity. A search for \"billing compliance,\" retrieves pages containing descriptions of compliance programs at universities, descriptions of compliance training courses, etc.
3. \"GoogleWashing.\" GoogleWashing is a process of altering online associations with a keyword by prolific linking using external sources, e.g., blogs. The downside of this process is that it excludes other high value reference sources, e.g., books or articles unavailable for Google indexing and promotes commercial sites only.
4. Commercialization instead of education. For example, a search for \"medical claims processing\" retrieves \"tips on starting a medical claims processing business\" and advertising for companies looking for billing salespeople and selling claims processing software and services, instead of references to and descriptions of processes or summarized product/service reviews.
5. Unknown credibility. Its responses do not have an external source of credibility rating. They are ranked according to a \"popularity\" formula.
6. No feedback. Limited and indirect ability to influence the search results by creating or deleting links. Complete lack of ability to modify search results or search terms.

Google helps us using the Internet as a source of collaborative but highly biased knowledge. For comparison, Google and book references occupy two opposing ends of spectrum of information materials arranged along credibility and relevance dimensions.

Wiki - Shared Knowledge Repository on the Web

Wiki technology aims at the middle ground between the extremes of the spectrum defined by Google and book encyclopedias. Wiki takes the concept of shared inter-article repository on the Web to the next, intra-article level, allowing multiple contributors share editing process of the same document. By localizing and focusing shared efforts to the same documents, the resulting sites achieve relevance degrees unobservable to popularity-based search engines.

Wikipedia is the most popular example of Wiki: BBC News has called Wikipedia \"one of the most reliably useful sources of information around, on or off-line.\" Wikipedia, like numerous other wikis, is void of commercialization and link-driven popularization. However, Wikipedia has two other problems:

1. \r Limited depth in spite of data availability on the Internet. For instance, a search for \"Straight Through Billing,\" \"Billing Compliance,\" or \"Medical Billing Transparency\" returns no results.\r
2. Limited search capabilities, most notably sharing with Google low precision and lack of context.\r

Search experts used specialization to approach both problems above. Separate specialized wikis for reference and education solve limited depth problem and separate specialized search engines solve limited search problem. For instance, BillingWiki.com is a result of collaborative effort to document a narrowly defined body of knowledge related to medical billing. As a Wiki, it started without a popularity-based search engine, relying on its narrowly defined body of knowledge, which allows deeper penetration into specific topics. It is easy to see the emergence of multiple wikis, each dedicated to narrowly defined, specific topic.

BillingWiki Enhanced with Specialized Billing Search Engine

A special purpose Billing Search Engine integrated within BillingWiki allows the members of a community interested in medical billing to improve their search experience. Such reinforced BillingWiki solves several shared shortcomings of Google and Wiki, while retaining key advantages of collaborating and specialization.

A major advantage of Billing Search Engine over standard Google\'s search is the ability to allow users to indicate satisfaction with the search result and to influence its future behavior. Specifically, Billing Search Engine allows to:

\r
1. \r Edit result relevance by promoting or demoting entries on the result page.\r
2. Create and maintain dynamic context, eliminating non-relevant results.
3. \"Learn\" from the community by continuously enhancing the set of search terms both by adding/deleting search terms and modifying their relative importance.
4. Edit and visually communicate the degree of relevance of the search terms. The search engine displays search terms and modifies their font size (color) to reflect relative term popularity.

For example, a search for \"Billing Compliance\" using specialized Billing Search Engine returns pages containing compliance solutions, descriptions of compliance terminology, audit risk management, and numerous examples of and press releases about billing compliance violations.

Summary

Google search may not be the optimal solution for professionals interested in most current reference material and innovations in straight through medical billing, compliance management, billing transparency, preferential patient scheduling, electronic medical records, and computer aided coding. A two-component architecture of the new generation billing knowledge archival systems, such as BillingWiki.com, offers multiple solution improvements. It includes a communal billing knowledge base and a specialized search engine. By facilitating continuous improvement and learning from its own users community, the resulting wiki performance outpaces Google\'s performance for medical billing knowledge queries.

References

\r
1. \r Antonio Gulli and Alessio Signorini, \"The Indexable Web is More Than 11.5 Billion Pages,\" http://www.cs.uiowa.edu/~asignori/web-size/\r
\r
2. Eszter Hargittai, \"Life beyond Google,\" http://news.bbc.co.uk/2/hi/technology/3601371.stm

Yuval Lirov, PhD, author of \"Practicing Profitability - Network Effect for Revenue Cycle Control in Healthcare Clinic and Chiropractic Office: Scheduling, SOAP Notes, Care Plans, Coding, Billing, Collections, and Audit Risk\" (Affinity Billing) and \"Mission Critical Systems Management\" (Prentice Hall), inventor of patents in Artificial Intelligence and Computer Security, and CEO of Vericle.net - Distributed Billing and Practice Management Technologies. Yuval invites you to register to the next webinar on audit risk at BillingPrecision.com.

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