The Augvest discussion held on 27 September 2018 featured Steve Liou (SL) from Guidepoint, Alan Freeman (AF) from 7Park, and Andrew Goldberg (AG) from HealthVerity (HV).
What are the challenges in working with healthcare data?
SL: Fragmentation in the industry makes it challenging to aggregate purchasing data from hospitals in a timely manner. Sourcing data from 1,500 hospitals makes the data cleaning and processing aspect time and labor intensive. Unlike therapeutics, difficult to find reliable data among medical devices.
AF: Most large players track retail, pharma etc – the prescription side of the business. Difficult to find data outside that. Healthcare laws make it challenging to consolidate data.
AG: Requires knowledge of the appropriate source to answer a specific question: ex physician-administered drug injection data will be found in medical claims, oral will be found in pharmacy claims. HV has data on 330 million individuals in the US and the majority of physicians.
How do use cases for your data differ between your corporate and investor clients?
SL: Corporate clients want to know market share trends relative to competitors, product pricing by SKU, and tracking sales of new competitive launches. Investors are typically more focused on predicting revenues for the upcoming quarter.
AF: Fundamental PMs want data to be easily digestible, quant PMs want data to be granular and mapped to company and ticker. 7Park sells data in raw and indexed forms, have observed that many clients prefer to see data in an indexed and clean bulk deliverable format. Another key difference with buyside clients: they can test validity of data against a ground-truth / benchmark, corporate investors have no benchmark.
AG: Corporates care about patient longitudinal analysis, investors care about what sells. Data completeness (“How much of the market does your data capture?”) is impossible to answer, but something that investors care a lot about.
Would you say the size and makeup of the populations you have data on is changing quickly?
SL: Our overall hospital panel is stable, but for rapid growth segments such as neurovascular or transcatheter heart valves, you can see a significant increase in a subset of hospitals adopting these new devices.
AF: Our clinic base is fairly stable with coverage all across the USA. This allows us to get a good view on oncology treatments which are not uniform across all states in the USA.
AG: Depends on the dataset – and we have over 50. Moreover, combining data (credit card, ‘death data’, lab data) adds more value through color than a singular index. Especially combining consumer and clinical instead of looking exclusively at clinical. Difficult to provide consumer data direct to investors while being HIPAA compliant but HV does have an analytic platform (using R) where consumer data specifically can be analyzed with.
What have you seen your data used for beyond nowcasting company revenues?
SL: Beyond total company revenue, forecasting sales for each revenue division is a value add. In addition, tracking the sales of individual products, especially for new approvals, can be particularly valuable. Monitoring pricing trends is also an important feature of our data, which can be informative in cases where certain manufacturers are discounting aggressively or bundling products in order to gain market share.
AF: Three use cases come to mind:
Market share analysis in a product category – e.g. PARP Inhibitors. View competitive behavior across drugs / companies.
Line of Therapy Analysis – is a drug being used sooner or later for treatments?
Indication of whether a drug is being used for off label diagnosis at higher volume – this will no show up in revenues immediately.
AG: Breadth of information is critical. Some datasets can initially seem unrelated, but can often move the needle in an investment – for instance, one can look at trends in physician behavior to see how that could potentially forecast revenue.
How do you present information to investors?
SL: Concise and accurate summary is critical. Investors are busy; one must provide data in an easily digestible manner (ex: excel model). For the busiest PM, revenue projections are provided by division and are updated every week until a company reports. For PMs with more bandwidth, we also provide the full backup data in Excel if they prefer to do additional analysis on their own. For quant funds, we can provide the consolidated data via a flat file every week.
AF: Offer raw and indexed data in excel, API or bulk S3. Don’t provide long form PDF - analysts lack time to read through. We have a quick headline summary that goes out with our excel product.
AG: Data is raw and available in a csv or flatfile via SFTP, but not as curated as offerings from 7Park or Guidepoint. Difficult to penetrate buyside as investors are often reluctant to divulge specifics about what they’re looking at and the questions they are trying to answer, making it difficult to recommend the right data type mix. HV does have unique datasets for lab (indicative of diagnosis rate or drug effectiveness) or branded medical devices that do offer very unique insights not generally found on the street.
Do you provide visibility into how you source your data?
SL: We partner with vendors who have access to this raw data via links to hospital ERP systems. Our expertise is taking that raw data and then validating and mapping each transaction to manufacturer catalogs. Some manufacturers have hundreds of thousands of product SKUs, thus this process can be laborious and time intensive.
AF: We will partner with our clients’ compliance teams to get comfort with data sources. We prescreen and vet all datasets from compliance and other points of view before using.
AG: Completely transparent about data source, although some prefer to be anonymous. Data is sourced directly from large players running lab tests etc. HV collects demographic info – age, gender, zip code. HV cypher allows you to append consumer info to find similar cohorts.
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