{"id":12345,"date":"2014-08-22T11:59:32","date_gmt":"2014-08-22T15:59:32","guid":{"rendered":"http:\/\/n2value.com\/blog\/?p=12345"},"modified":"2014-11-10T09:57:25","modified_gmt":"2014-11-10T14:57:25","slug":"some-thoughts-on-revenue-cycle-management-predictive-analytics-and-competing-algorithms","status":"publish","type":"post","link":"https:\/\/n2value.com\/blog\/some-thoughts-on-revenue-cycle-management-predictive-analytics-and-competing-algorithms\/","title":{"rendered":"Some thoughts on Revenue Cycle Management, predictive analytics, and competing algorithms"},"content":{"rendered":"

After some reflection, this is clearly Part 5 of the “What medicine can learn from Wall Street” series.<\/strong><\/p>\n

It occurred to me while thinking about the staid subject of revenue cycle management (RCM<\/strong>) that this is a likely hotspot for analytics. \u00a0 First, there is data – tons of data. \u00a0Second, it is relevant – folks tend to care about payment.<\/p>\n

RCM<\/strong> is the method by which healthcare providers get paid, beginning from patient contact, leading to evaluation and treatment, and submitting charges by which we are ultimately paid via contractual obligation. \u00a0Modern RCM goes beyond billing, to include marketing, pre-authorization, completeness in the medical records to decrease denials, and \u2018working\u2019 the claims until payment is made.<\/p>\n

Providers get paid by making claims. \u00a0Insurers \u2018keep the providers honest\u2019 by claim denials when the claims are not properly 1) pre-authorized, 2)documented, 3)medically indicated (etc). \u00a0There is a tug of war between both entities, which usually results in a relationship that ranges somewhere between grudging wariness to outright war (with contracts terminated and legal challenges fired off). \u00a0The providers profit by extracting the maximum profit they are contractually allowed to, the insurer by denying payment so that they can obtain investment returns on the pool of reserves they have. \u00a0Typically, the larger the reserve pool, the larger the profit.
\nInsurers silently fume at \u2018creative coding\u2019 where a change of coding rules causes a procedure\/illness that has previously been paid at a lower level now paid at a much higher level. \u00a0Providers seethe at \u2018capricious\u2019 denials which require\u00a0 staff work to provide whatever documentation requested (perhaps relevant, perhaps not) and \u2018gotcha\u2019 downcoding due to a single missing piece of information. \u00a0In any case, there is plenty of work for the billing & IT folks on either side.<\/p>\n

Computerized revenue cycle management seems like a solution until you realize that the business model of either entity has not changed, and now the same techniques on either side can be automated.\u00a0 Unfortunately, if the other guy does it, you probably need to too – here’s why.<\/p>\n

We could get into this scenario: \u00a0 A payor (insurer) when evaluating claims, decides that there is too much spend ($) on a particular ICD-9 diagnosis (or\u00a0ICD-10\u00a0if you prefer) than expected and targets these for claim denials. \u00a0A provider would submit claims for this group, be denied on many of them, re-submit, be denied, and then either start \u2018working\u2019 the claims to gain value from them or if they had a sloppy or lazy or limited billing department, simply let them go (with resultant loss of the claim). \u00a0That would be a 3-12 month process. \u00a0 However, a provider that was using descriptive analytics (see part 1<\/a>) on say a weekly or daily basis would be able to see something was wrong more quickly – probably within three months – and gear up for quicker recovery.\u00a0 A determined (and agressive) payor could shift their denial strategy to a different ICD-9 and something similar would occur. \u00a0After a few cycles of this, if the provider was really astute, they might data mine the denials to identify what codes were being denied and set up a predictive algorithm to compare new denials relative to their old book of business. \u00a0This would identify statistical anomalies in new claims, and could alert the provider about the algorithm the payor was using to target claims for denial. \u00a0By anticipating these denials, and either re-coding them or providing superior documentation to force the payor to pay (negating the beneficial effects of the payor\u2019s claim denial algo), claims are paid in a timely and expected manner. \u00a0I haven\u2019t checked out some of the larger vendors\u2019 RCM offerings but I suspect that this is not far in the offing.<\/p>\n

I could see a time where a very aggressive payor (perhaps under financial strain) strikes back with an algorithm designed to deny some, but not all claims on a semi-random basis to \u2018fly under the radar\u2019 and escape the provider\u2019s more simple detection algorithms. \u00a0A more sophisticated algorithm based upon anomaly detection techniques could then be used to identify these denials\u2026. \u00a0This seems like a nightmare to me. \u00a0Once things get to this point, it’s probably only a matter of time until these games are addressed by the legislature.<\/p>\n

Welcome to the battles of the competing algorithms. \u00a0This is what happens in high-frequency trading<\/a>. \u00a0Best algorithm wins, loser gets poorer.<\/strong><\/p>\n

One thing is sure: in negotiations, the party who holds & evaluates the data holds the advantage. \u00a0 The other party will forever be negotiating from behind..<\/p>\n

P.S.\u00a0 As an aside, with the ultra-low short term interest rates after the 2008 financial crisis, the time value of money is near all-time lows. \u00a0Delayed payments are an annoyance but apart from a cash-flow basis there is not any real advantage to delaying payments. \u00a0 Senior management who lived through or studied the higher short term interest rates of the 1970’s-1980’s will recall the importance of managing the ‘float’ and good treasury\/receivables operations. \u00a0Changing economic conditions could make this even more of a hot topic.<\/p>\n","protected":false},"excerpt":{"rendered":"

After some reflection, this is clearly Part 5 of the “What medicine can learn from Wall Street” series. It occurred to me while thinking about the staid subject of revenue cycle management (RCM) that this is a likely hotspot for analytics. \u00a0 First, there is data – tons of data. \u00a0Second, it is relevant – […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"Some thoughts on Revenue Cycle Management, predictive #analytics, and competing algorithms #hcsm http:\/\/wp.me\/p4mtfP-3d7","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[8,3,6],"tags":[],"class_list":["post-12345","post","type-post","status-publish","format-standard","hentry","category-finance","category-physician-executives","category-process-analytics"],"jetpack_publicize_connections":[],"aioseo_notices":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4mtfP-3d7","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/12345","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/comments?post=12345"}],"version-history":[{"count":4,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/12345\/revisions"}],"predecessor-version":[{"id":12586,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/12345\/revisions\/12586"}],"wp:attachment":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/media?parent=12345"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/categories?post=12345"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/tags?post=12345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}