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	<title>Comments for HIStech Report</title>
	<link>http://histechreport.com</link>
	<description>Healthcare IT solutions news from HIStalk</description>
	<pubDate>Fri, 04 Jul 2008 14:44:58 +0000</pubDate>
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		<title>Comment on SCI Solutions, a Pioneer in Internet-Based Healthcare Applications, Focuses Its Talents on Access Management and Revenue Cycle Management Solutions, Preparing Hospitals for Consumer-Driven Healthcare by Ian Furst</title>
		<link>http://histechreport.com/2008/02/22/sci-solutions-a-pioneer-in-internet-based-healthcare-applications-focuses-its-talents-on-access-management-and-revenue-cycle-management-solutions-preparing-hospitals-for-consumer-driven-healthcare/#comment-141</link>
		<dc:creator>Ian Furst</dc:creator>
		<pubDate>Thu, 21 Feb 2008 11:12:00 +0000</pubDate>
		<guid>http://histechreport.com/2008/02/22/sci-solutions-a-pioneer-in-internet-based-healthcare-applications-focuses-its-talents-on-access-management-and-revenue-cycle-management-solutions-preparing-hospitals-for-consumer-driven-healthcare/#comment-141</guid>
		<description>I think that one of the key elements in revenue cycles is the balance between wait and resources.  In socialized medicine it is an issue due to cost and in private it drives revenue.  It will be interesting to see how this sector merges data from the scheduler and patient side.  In theory, you could use queuing theory to predict waiting times for patients by using data from the scheduler using upcoming demand and availabiliy.  I doubt in house applications would have the technical or theoritical knowledge base to do it but in an application like this it might get done.   We currently due it for limited applications (and find it difficult to progam) but with a unified scheduler and usage data it should be easier. Thanks Ian.
&lt;a href="http://www.waittimes.blogspot.com/" rel="nofollow"&gt;www.waittimes.blogspot.com&lt;/a&gt;</description>
		<content:encoded><![CDATA[<p>I think that one of the key elements in revenue cycles is the balance between wait and resources.  In socialized medicine it is an issue due to cost and in private it drives revenue.  It will be interesting to see how this sector merges data from the scheduler and patient side.  In theory, you could use queuing theory to predict waiting times for patients by using data from the scheduler using upcoming demand and availabiliy.  I doubt in house applications would have the technical or theoritical knowledge base to do it but in an application like this it might get done.   We currently due it for limited applications (and find it difficult to progam) but with a unified scheduler and usage data it should be easier. Thanks Ian.<br />
<a href="http://www.waittimes.blogspot.com/" rel="nofollow">www.waittimes.blogspot.com</a></p>
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