Diagnosis Progression Patterns using Claims Data

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by Randolph Schilke1 view·Updated Aug 26, 2021
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  1. Yeah. Hello everyone.
  2. My similar project is titled diagnosis progression patterns.
  3. Hello everyone.
  4. My similar project is tiled diagnosis progression patterns using claims
  5. data. I am Randolph Shelby a data scientist within guide.
  6. Well, I'm a peer reviewed scientific author with numerous years
  7. of experience in the health care.
  8. I'm a peer review scientific.
  9. I'm Randolph schulke.
  10. I'm Randolph schulke a data scientist within guide.
  11. Well, I'm a peer reviewed scientific author with numerous years
  12. of experience in the health care industry ranging from hospitals,
  13. state Department of Health and now insurance.
  14. Did you know the average progression duration for a disease
  15. may be approximately seven years and as a consequence, God
  16. well is failing to intervene with God well is failing
  17. to intervene guide well, is failing to intervene with at
  18. risk members before disease progression is reversible.
  19. The early detection disease before symptoms or signs appear can
  20. contribute to the improvement of outcomes and survival rates of
  21. our members.
  22. Let's take a look at a simple stroke.
  23. Example, On average .23% of the population will experience a
  24. first time stroke or roughly 10,000 of our members on
  25. average costing us on average.
  26. Pointy On average .23% of the population.
  27. Or 10,000 of our members will experience a first time
  28. stroke costing four blue.
  29. An estimated $20-$40,000 per person.
  30. Of these.
  31. 10,000 floatable members.
  32. An estimated 23% will have a secondary stroke of these
  33. secondary stroke members, one in five of these secondary stroke
  34. members. 1, 5 of these secondary stroke members 1 5
  35. could be preventable when members achieve optimal Of these secondary
  36. stroke appearances.
  37. One in five or 460 could be preventable when members
  38. achieve optimal awareness of their disease, resulting in estimated annual
  39. minimal, resulting in estimated $9.2 million savings.
  40. Did you also know that dis enrollment maker when members
  41. encounter unexpected more abilities and their health plans are stretched
  42. beyond their expectations.
  43. Plain and simple dis enrollment rates among Medicare members are
  44. not equal.
  45. Plain and simple.
  46. Dissemble marines are not the same.
  47. Plain and simple.
  48. Dis enrollment rates among Medicare members are not equal when
  49. comparing planned benefit packages.
  50. An unanticipated healthcare utilization within an inappropriate benefit plan package
  51. may promote dis enrollment.
  52. So what is the problem?
  53. So what's the problem?
  54. Yeah, simply people don't know what they don't know.
  55. 80% of survey members believe that they are aware of.
  56. 80% of survey members believe that they are aware of
  57. their at risk diseases.
  58. However, there simply people don't want, simply, people don't know
  59. what they don't know.
  60. 80% of surveyed members believe that they are aware of
  61. all their at risk diseases.
  62. However, they also believe their current diseases were unexpected and
  63. caught them off guard.
  64. Our members are helplessly unaware of what they don't know.
  65. Our members are helplessly unaware of what they don't know
  66. in our need of our guidance today.
  67. I want to demonstrate to all of you a risk
  68. profile web page for all our members that will display
  69. financial health risks.
  70. Today I want to just her today.
  71. Today I want to demonstrate to all of you a
  72. Yeah today I want to demonstrate to all of you
  73. a rich profile web page for all our members that
  74. displays financial and health risks for the upcoming years.
  75. That will enable members to understand if their current health
  76. product is appropriate and highlight upcoming at risk diseases.
  77. Let's walk a mile in our member shoes to learn
  78. how this will look and feel.
  79. Upon logging to the member homepage, the members will be
  80. greeted with a brand new tab titled health and financial
  81. risks where members will be able to view their estimated
  82. health risks and associated financial impact.
  83. Let's click and learn more.
  84. This web page will adjust this web page will ingest
  85. machine learning predictions updated daily to provide members with the
  86. real time forecasting.
  87. For example, we can toggle predictions that will happen within
  88. 1 to 3 years, sort by your service family member
  89. health risk an estimated claim health risks will automatically be
  90. hyperlinked to learn more and connect with health experts.
  91. Most importantly, most importantly, members will be able to see
  92. in real time estimates of their current plan use Most
  93. important, most importantly, members will be able to see in
  94. real time estimates of their current plan.
  95. Use for these risks, they will be prompted with their
  96. product matching algorithm to suggest plans which will lower their
  97. estimated out of pocket costs or align with their health
  98. realization needs.
  99. I estimate there are 800,000 active floor blue members Who
  100. regularly engage with our online website in 2021.
  101. Thanks. In 2021 there's an estimated 800,000 floor blue members
  102. who actively engaged with our member website.
  103. And according to the medical journal Lancet, 27% of all
  104. health care expenditures and according to the medical journal last
  105. set, an estimated 27% of all health care expenditures were
  106. due to preventable illnesses.
  107. According to the medical journal Lancet, 27% of healthcare spending
  108. was due to these preventable illnesses.
  109. We could anticipate that on a yearly average 45,000 of
  110. our health product members could prevent comorbidities and unexpected financial
  111. costs. Additionally, members that actually engaged.
  112. Additionally, members that actually engage with our plants suggestion tool
  113. and also their plan according to the essential health needs
  114. that are expected.
  115. Members that are uh huh.
  116. Members that actually engage with our plans, suggestion tool.
  117. Members that actively engaged with our plans suggesting tool and
  118. there members are actually engaged with our plans suggesting tool
  119. and alter their plan According to the essential health needs
  120. are expected to reduce their dis enrollment rate by 50%.
  121. Yeah. Of the five active for blue members that I
  122. interviewed 100%.
  123. 1 understand more about what they are based up within
  124. five years.
  125. 40% 4%.
  126. Don't believe there's enough financial information.
  127. 40%. Don't believe there's enough finance information available to them
  128. on the member website.
  129. An astounding, 80% would switch to health plan if they
  130. knew in advance what they're expected costs were for the
  131. upcoming year.
  132. In particular.
  133. One member who set out to me in particular.
  134. One member who stood out to me said that our
  135. sales representative walked her through her estimated upcoming costs and
  136. adjusted her plan and now she is excited and happy
  137. that with a plan that she's in in particular.
  138. One member who received one on one consultation about their
  139. health plans switched after learning the third plan was more
  140. in line with that her needs in particular In particular
  141. one member who received one on 1 consultation about her
  142. health plan switched.
  143. That was more in line with her needs.
  144. She even complimented this.
  145. No idea that replicated that sales agents job that aligned
  146. her plan with what she's expecting to have.
  147. Yeah. Additionally, when researching additionally, when researching customers, Net promoter
  148. score, which quantifies how happy members are with our plans
  149. and we suggest them to family members and friends.
  150. I found that those that are unhappy almost I found
  151. those that were unhappy.
  152. I found that those that were unhappy are almost when
  153. researching Cosmo's net promoter score.
  154. I found when researching customers.
  155. Net promoter score which quantifies how happy members are with
  156. their products when researching customers.
  157. Net promoter score, which quantifies how happy customers are with
  158. our products and that they would recommend to their friends
  159. and family.
  160. I found the does that were when researching customers?
  161. Net promoter score.
  162. When researching customers.
  163. Net promoter score, which quantifies.
  164. Mm. Yeah.
  165. When researching customers.
  166. Net promoter score, which quantifies how happy members are with
  167. our products and recommend them to their friends and family.
  168. I found that those that are unhappy are almost twice
  169. as likely to dis enroll more.
  170. So members implant betha packages that don't cover all their
  171. needs are more likely to dis enroll today.
  172. The only risk.
  173. Mhm. Today, the only rip today.
  174. The only current risks are sample sized and learning if
  175. these findings are consistent amongst them.
  176. Today, the only current risk is our sample size and
  177. learning. If these.
  178. Uh huh.
  179. Today, the only current risks, our sample size and learning.
  180. These findings remain consistent among segments.
  181. Right now.
  182. I need $15,000 in two months.
  183. This will allow me to conduct additional validation among our
  184. members and with the net more score buckets to learn
  185. if this enrollment rates could be altered among those with
  186. a low opinion.
  187. Thank you