Payers and Providers

  • Strategic Planning
  • New Entrant Research
  • System & Vendor Selection
  • Transformation Initiatives

Growth-Stage Companies

  • Executive Leadership
  • Scaling IT & Operations
  • Strategic Partnerships

P/E & Venture Firms

  • Technology Due Diligence
  • New Entrant Research
  • Portfolio Company Leadership
  • HIT Industry Perspectives

Scott Booher is a healthcare business and technology leader with a 20-year history of scaling and maturing organizations, spanning enterprise to growth-stage companies:

  • COO at Zipnosis, a virtual care SaaS platform, providing transformative leadership and client/investor confidence in the ability to execute, leading to a successful Series A financing round.
  • SVP at Ascension Health, providing technology leadership and business opportunity assessment for a spin-off of the nation’s largest non-profit health system.
  • SVP/CIO at Medica Health Plans, where he built the team, technology infrastructure and strategic partnerships necessary to support a $2B managed care organization spin-off.
  • VP of Application Development at UnitedHealth Group.
  • President of ITR Mobility, where he led the transformation of a mobile development firm with unique IP into an authentic product and services company with accelerating sales growth, compelling products and maturing operations.

Scott has a deep understanding of the business and technology drivers supporting the financing and delivery of healthcare today – an essential quality for senior leaders during this period of disruptive change.

Building a Healthcare IT Research Tool

A flood of news is being generated on the U.S. healthcare system, coming from hundreds of disparate news sources. Thousands of healthcare stories appear each day, making it difficult to get a real sense of what’s happening in a particular focus area, let alone identify quality content for review.

Financing events are only a small part of the picture. Health Systems, Payers, Capital Firms and other stakeholders have critical questions such as:

  • What is happening within a particular healthcare topic?
  • What new players are in this space?
  • What vendor deals, installations and partnerships have occurred?

In work with healthcare firms these questions often require significant staff (or consultant) time to resolve. The goal of this tool is to provide assistance to research efforts within the healthcare IT field, reducing analysis load while providing a simple method to follow an emerging healthcare topic or player. The platform curates 300,000 news stories across 1,000 healthcare terms and 1,500 solution partners, and continues to expand.

High Score: An attempt is made to fill the news container with the highest-scoring news items available, in reverse chronological order. Lower-quality news may be supplemented to fill out the container when necessary. Depending on the frequency of news generated for a particular item, its news summary may represent a few days, or a few months’ activity. It can be difficult to discern how an item may be linked to other topics/partners/stakeholders based only on a headline - where possible a Label is displayed that illustrates the specific connections between this record and others.

Filter Linked: Similar to the above, but filtered to only those news items for a which a Label can be displayed to illustrate the specific connections between this and other topics/partners/stakeholders.

Recently Active: Those items with the highest frequency of Authoritative news volume over the last week. A combination of always-popular topics (i.e. Mental Health) as well as short-term news spikes.

Incr. News Volume / Volume Trending: Items with a material uptake in Authoritative news volume over the last 30 days, or over historical norms.

Top 100 News Score: The Top 100 entities for Authoritative news volume for the last 180 days. This score is swayed by many factors including the start date within the platform and changes to the Authoritative Source list.

Authoritative Sources: This classification has both objective and subjective components. Objective in that each source must meet the same quality and volume thresholds; Subjective in that each source gets a perceived authoritative ranking, i.e. a major newspaper versus a local news station that simply reprints press releases. There are currently 100+ ‘Authoritative’ (out of 5,000+) unique healthcare news sources, and this list is reviewed weekly.

Top News Sources: A ranked list of the top Authoritative Sources for each Topic, Partner or Organization. Updated weekly.

Mirrored Topics: A consumer searching the web for news on one term, would generally expect similar results for the other term in this relationship, i.e. ‘EHR’ and ‘Electronic Health Record’. In other cases the mapped Topics are separate terms but are now used interchangeably in healthcare news, such as with ‘Telehealth’ and ‘Virtual Care’. High-scoring news for one Topic will be mapped to its mirror(s). Solution Partners linked to one Topic will also be mapped to their mirror(s).

Related Topics: A subjective mapping of other topics that might be of interest.

Basic Connections:

  • Solution Partner Categorization [Topic <> Solution Partner]: This is generally a list of the products/services claimed by the Partner, although that is an imperfect process. Classification cues are pulled from the Partner web site, Twitter bio and published new stories. The goal is to err on the side of inclusiveness rather than impose a rigid, limited classification model. (side note: Spend 5 minutes browsing the website of many new healthcare entrants and one may still have little idea what services they provide to industry.) Each Solution Partner will begin with at least (1) Topic classification, and will broaden out as news stories place it into additional categories.

  • Financing Event [Capital Firm <> Solution Partner]: Historically this has been the focus of much of the press on new healthcare entrants, and there are several firms already specializing in this information. While many financing linkages are documented, it is not the primary focus of this tool.

  • Health System Implementation [Health System <> Solution Partner]: This information is generally pulled via algorithm from press releases and news stories that mention the partnership or implementation. Please see the [Filter: Linked News] tab if present for the specific news items establishing these links.

  • Payer Implementation [Payer Organization <> Solution Partner]: This information is generally pulled via algorithm from press releases and news stories that mention the partnership or implementation. Please see the [Filter: Linked News] tab if present for the specific news items establishing these links.

Interest Connections:

  • Capital Interest [Capital Firm <> Topic]

  • Health System Interest [Health System <> Topic]

  • Payer Interest [Payer Organization <> Topic]

  • There are two sources of connections herein. The first are pulled via algorithm from news and press releases: a Capital Firm focusing on a particular Topic such as ‘Blockchain for Healthcare’, or a Health System implementing a new ‘Patient Portal’. The second driver is via extrapolation: A Capital Firm investing in a Solution Partner who provides ‘Revenue Cycle Management’ (RCM) services, may also get a direct linkage to RCM over time. Please see the [Filter: Linked News] tab if present for the specific news items establishing these links.

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Methodology

News items are selected via search algorithm and then scored across several factors, including the perceived quality of the source. Depending on the frequency of news generated for a particular item, its news summary may represent a few days or a few months’ activity.

Links between Topics, Solution Partners, Health Systems, Payer Organizations and Capital Firms are determined through manual review as well as via algorithm. As connections between entities are established, ‘serendipitous exploration’ will become possible across data elements.

Although some direct testing occurs, note that with the incoming volume of data (5,000 news items per day) it would be infeasible to sample each record, and the odd categorization error is likely. This site is updated weekly, and your comments are welcome.