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Elizabeth Holmes aims to undercut prosecutions claim of deception

Elizabeth Holmestestified in theTheranosfraud trial Monday that the companys blood-testing technology performed well in early studies with drug makers such asMerck,Astra Zeneca, andBristol Myers Squibb. The line of questioning by the defense fielded with slow, steady replies from Holmes was meant to rebut testimony by prosecution witnesses suggesting the company sent falsified reports on validation testing by drug companies to investors. Holmes also countered testimony from aPfizerdirector, Shane Weber, who told jurors that Theranos replies to his technical questions were at times deflective or evasive.

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By calling Holmes to testify, her lawyers are betting that she will be able cast doubt on prosecutors narrative that she deliberately misled investors and the companys potential clients. The big question is how she will hold up under cross examination.

Biotechs big data problem is all too human

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As biological datasets have exploded in size, biotechs need data scientists to process their plentiful petabytes. But many are struggling to hire enough researchers with the computational know-how needed for the job. Executives believe the gap might [become] fourfold unless they start acting now, Parag Patel, a partner atMcKinsey and Companyfocusing on the life sciences industry, toldSTAT. Part of the problem is supply and demand: There are only so many top-tier data scientists, and pharma is competing for them against companies likeGoogle,Apple, andFacebookthat cannot only pay more, but are often more data-proficient, too. It can get frustrating just cleaning up dirty data, said Neal Cheng, a data scientist who left biotech to work at eBay.Read more about the biotech brain crunch in our colleagueAngus Chens new story.

Verilys clinical trials business leans into data

At theSTATSummitlast week, Katie spoke withVerilys leaders about their plans for their clinical trials infrastructure, just one arm of the life science companys sprawling and sometimes unfocused business.Amy Abernethy, who joined theAlphabetsubsidiary over the summer after championing real-world data and evidence at theFDAas principal deputy commissioner, described her next steps as president of clinical trials platforms. Practically speaking, we are going to focus on building longitudinal datasets that can be put to use for understanding the contours of illness, said Abernethy, adding that such data could be used to test multiple different treatments simultaneously. Abernethy said the company hopes to create a vast repository combining real-world data, medical records, and traditional clinical trial data. Mario has moredetails.

The dream of data-sharing in 2030

By the end of this decade, publichealthresponse and preparedness will be driven by access to real-time data. In this day and age, that seems like a fairly basic desire. But it is among many distant goals that made it onto anew interoperability wish listcompiled by theOffice of the National CoordinatorforHealthIT. To help guide its policy roadmap, ONC asked providers and other stakeholders to list outcomes they hope to achieve through enhanced data interoperability by 2030. The agency received more than 700 responses. Among the other priorities on the list: Patients and doctors will be able to compare the costs of drugs, tests, and procedures online before the bill comes in.

Epics sepsis alerts skyrocketed during the pandemic

A general rule for AI models is that they should be trained on data from the kind of patients they are likely to see in real clinical settings. But what happens when the AI encounters a fundamental change, like the onset of Covid-19? Anew studyof a sepsis algorithmdeveloped byEpic Systemsfound that the number of alerts it generated in two dozen hospitals jumped 43% in the first three weeks after Covid-19 hit. The study by researchers at theUniversity of Michigandid not measure the accuracy of the alerts, but pointed out the increased volume may contribute to alert fatigue and prove detrimental at a time of constrained resources. The finding also points to the need for careful monitoring and governance of AI algorithms after they are implemented.

Meanwhile, clinical predictive tools continue to be adopted around the country, asnew survey datafrom theCollege of Healthcare Information Management Executivesshows. In the last year, the percentage of surveyed acute care organizations with predictive tools integrated into clinician workflows increased from 44% to 55% using a mix of homegrown tools along with those from EHRs and third parties. Among ambulatory care providers, adoption was at 52%, and at 40% in long-term post-acute care facilities.

Athenahealths acquisition & moves for AI drug discovery

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Elizabeth Holmes testifies in Theranos trial & Athenahealth acquisition 2.0 - STAT