{"id":13120,"date":"2017-07-31T14:41:47","date_gmt":"2017-07-31T18:41:47","guid":{"rendered":"http:\/\/n2value.com\/blog\/?p=13120"},"modified":"2017-07-31T14:41:47","modified_gmt":"2017-07-31T18:41:47","slug":"whats-up-with-n2value-tying-up-loose-ends","status":"publish","type":"post","link":"https:\/\/n2value.com\/blog\/whats-up-with-n2value-tying-up-loose-ends\/","title":{"rendered":"What\u2019s up with N2Value -tying up loose ends"},"content":{"rendered":"

\"Dora<\/a><\/p>\n

It\u2019s been almost a year since my last long-form article. Of course, \u2018busyness\u2019 in real life and blog writing are inversely proportional! I\u2019ve been focused on real-life advances; namely neural networks, machine learning, and machine intelligence which fall loosely under the colloquial misnomer of “A.I.”<\/p>\n

After a deep dive into machine learning, it is contemporaneously unexpectedly simple and deceptively difficult. The technical hurdles are significant, but improving \u2013 math skills ease the conceptual framework, but without the programming chops, practical application is tougher. Worse, the IT task of getting multiple languages, packages, and pieces of hardware to work together well is daunting. Getting the venerable MNIST to work on your computer with your GPU might be a weekend project \u2013 or worse. I\u2019m not a \u2018gamer\u2019, so for the last decade it has been hard for me to get excited about increasing CPU clock speeds, faster DRAM, and faster GPU flops. Like many, I\u2019ve been happy to use OSX on increasingly venerable Mac products \u2013 works fine for my purposes.<\/p>\n

But since Alexnet\u2019s publication in 2014, the explosion in both theory and application in machine learning has made me sit up and take notice. The Imagenet Large Scale Visual Recognition Challenge top-5 classification error rate was only 2.7% in latest competition held a few days ago in July 2017. That\u2019s up from 30%+ error rates only four years ago. And my current hardware isn\u2019t up to that task.<\/p>\n

So, count me in. Certainly AI will be used in healthcare, but in what manner and to what extent still to be worked out. Pioneer firms like Arterys and Zebra Medical Vision, brave uncharted regulatory waters, watched closely by AI startups with similar dreams.<\/p>\n

So, while I\u2019d like to talk more about AI, I\u2019m not sure that N2Value is the right place to do it. N2Value is primarily a healthcare thought leadership blog, <\/b>promoting an evolution from<\/b> Six Sigma <\/b>methodology <\/b>into more robust management practices <\/a><\/b>which incorporate systems theory<\/b>, focus on appropriately chosen metrics, <\/b>model<\/b> patient populations and likely outcomes and <\/b>thereby successfully implement profitable <\/a><\/b>value-based care.<\/b> Caveat: with current US politics, it is very difficult to predict healthcare policy\u2019s direction.<\/p>\n

So, in the near future, I will decide what the scope of N2Value is to be going forward. I thank my loyal readers & subscribers who have given me 5 digit page views over the short life of the blog \u2013 far more than I ever expected! The blog has been a labor of love, but I’m pretty sure that AI algorithms have a place in healthcare management. However, I am not sure if you want to hear me opine on which version of convolutional neural network works better with or without LSTM added here, so stay tuned!<\/p>\n

I have a few topics I have eluded to which I would like to mention quickly as stubs \u2013 they may or may not be expanded in the future.<\/p>\n

STUB: What Healthcare can learn from Wall Street<\/a>.<\/b><\/p>\n

<\/b>The main point of this series was to document the chronological implications of advances in computing technology on a leading industry (finance), to describe the likely similar path of a lagging industry (healthcare). I never was able to find the statistics on Wall Street employment I was seeking, which would document a declining number of workers, while documenting higher productivity and profitability per employee as IT advances allowed for super-empowerment of individuals.<\/p>\n

Additionally, it raised issues regarding technology in B2B relationships that are adversarial. Much like Insurer-Hospital or Hospital-Doctor. If I have time, I\u2019d like to rewrite this series. It was when I first began blogging and it is a bit rough.<\/p>\n

STUB: The Measure is the Metric<\/a><\/b><\/p>\n

One of my favorite articles (with its siblings), this subject was addressed much more eloquently on the Ribbonfarm blog by David Manheim in Goodhart\u2019s Law and why measurement is Hard<\/a>. If anything, after reading that essay, you will have sympathy for the metrics-oriented manager and be convinced that nothing they can do is right. I firmly believe that metrics should be designed to the task at hand, and then once achieved, monitored for a while but not dogmatically so. Better to target new and improved metrics than enforce institutional petrification \u2018by the numbers.\u2019<\/p>\n

STUB: Value as Risk Series<\/a><\/b><\/p>\n

I perceive the only way for value based care to be long-term profitable\/successful is for large-scale vertical integration by a large Enterprise Health Institution (EHI) across the care spectrum. Hospital acquires Clinics, Practices, and Doctors, quantifies its covered lives, and then with better analytics than the insurers, capitates, ultimately contracting directly with employers & individuals. The insurers become redundant \u2013 and the Vertically Integrated Enterprise saves on economies of scale. It provides care in the most cost effective manner possible & closes beds, relying instead on telehealth, m health apps & predictive algorithms, and innovative care delivery.<\/p>\n

When the Hospital\u2019s profitability model resembles the insurer\u2019s, and it is beholden only to itself (capitated payments are all there is), something fascinating happens. No longer does it matter if there is an ICD-10\/HOPPS\/CPT\/DRG code for a procedure. The entity is no longer beholden to the rules of payment, and can internally innovate. A successful vertically integrated enterprise will \u2013 and quickly. While there will have to be appropriate regulatory oversight to prevent patient abuse, profiteering, or attempts to financialize the model; adjusting capitation with incentive payments for real measures of quality (not proxies) will prompt compliance and improved care.<\/p>\n

Writing as a physician, this arrangement may or may not commoditize care further. Concerns about standardization of care are probably overstated, as the first CDS tool more accurate than a physician will standardize care to that model anyway! From an administrator\u2019s perspective, it is a no-brainer to deliver care in an innovative manner that circumvents existing stumbling blocks. From a patient\u2019s perspective, while I prefer easy access to a physician, maintaining that access is becoming unaffordable, let alone then utilizing health care! At some point, the economic pain will be so high that patients will want alternatives they can afford. Whether that means mid-levels or AI algorithms only time will tell.<\/p>\n

STUB: Data Science and Radiology<\/a><\/b><\/p>\n

I really like the concept I began here with data visualization in five dimensions. Could this be a helpful additional tool to AI research like Tensorboard? I\u2019m thinking about eventually writing a paper on this one.<\/p>\n

STUB: Developing the Care Model<\/a><\/b><\/p>\n

The concept of treating a care model like an equation is what got me started on all this \u2013 describing a system as a mathematical model seemed like such a good idea \u2013 but required learning on my part. That, and the effects thereof, are still ongoing. At the time of the writing, the solution appeared daunting & I \u201cput the project on the back burner (i.e. abandoned it)\u201d as I couldn\u2019t make it work. Of course, with advancing tools and algorithms well suited to evaluation of this task, I might rexamine this soon.<\/p>\n","protected":false},"excerpt":{"rendered":"

It\u2019s been almost a year since my last long-form article. Of course, \u2018busyness\u2019 in real life and blog writing are inversely proportional! I\u2019ve been focused on real-life advances; namely neural networks, machine learning, and machine intelligence which fall loosely under the colloquial misnomer of “A.I.” After a deep dive into machine learning, it is contemporaneously […]<\/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":"New N2Value.com post - What\u2019s up with N2Value -tying up loose ends. #healthcare #AI #machinelearning","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[2,7],"tags":[],"class_list":["post-13120","post","type-post","status-publish","format-standard","hentry","category-healthcare","category-leadership"],"jetpack_publicize_connections":[],"aioseo_notices":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4mtfP-3pC","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/13120","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=13120"}],"version-history":[{"count":3,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/13120\/revisions"}],"predecessor-version":[{"id":13124,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/13120\/revisions\/13124"}],"wp:attachment":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/media?parent=13120"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/categories?post=13120"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/tags?post=13120"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}