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What Went Wrong...

Examining the missteps of various software products across industries reveals common pitfalls that can derail even the most promising innovations. From inadequate market research and poor user experience design to insufficient testing and failure to adapt to technological advancements, these challenges underscore the importance of thorough planning and execution. The following section outlines specific cases, offering insights into how these factors contributed to their downfall and the lessons that can be gleaned to inform future endeavors.

Available Lessons:

200

Genomic Risk Assessment Tool

BioTech

Genomic Health

The software aimed to assess genetic risk for diseases but faced criticism for oversimplified results and poor physician adoption.

WHAT WENT WRONG

  • Overly generic algorithms lacking individual context

  • Limited integration with EHR systems for clinicians

SIGNALS MISSED

  • Low satisfaction rates from clinicians and patients

  • High support tickets requesting detailed explanations

HOW COULD THEY HAVE AVOIDED THIS

  • Enhancing algorithms with personalized context

  • Testing integration workflows with real-world clinics

TEAMS INVOLVED

Product, AI, Data, Customer Success

Allscripts Sunrise (Early Versions)

BioTech

Allscripts

This electronic health record (EHR) software faced criticism for poor integration with existing systems and frequent crashes.

WHAT WENT WRONG

  • Technical instability during high usage

  • Limited compatibility with hospital infrastructure

SIGNALS MISSED

  • User complaints about data synchronization errors

  • Frequent support tickets from hospitals reporting outages

HOW COULD THEY HAVE AVOIDED THIS

  • Conducting stress tests for scalability

  • Enhancing compatibility through modular system designs

TEAMS INVOLVED

Product, Engineering, QA, Customer Success

Health Risk Reports (Initial Rollout)

BioTech

23andMe

The FDA halted the release of genetic health reports due to insufficient scientific validation and concerns about misleading consumers.

WHAT WENT WRONG

  • Poor regulatory compliance during product development

  • Overpromised results without adequate scientific backing

SIGNALS MISSED

  • Early FDA warnings about insufficient validation

  • Consumer feedback questioning the accuracy of results

HOW COULD THEY HAVE AVOIDED THIS

  • Engaging regulatory agencies proactively

  • Publishing data and methodology in peer-reviewed journals

TEAMS INVOLVED

Product, Engineering, Compliance, Marketing, CEO

Flatiron Clinical Trial Matching

BioTech

Flatiron Health

A software tool meant to match patients to clinical trials struggled with inaccurate matches due to incomplete data integration.

WHAT WENT WRONG

  • Poor data harmonization across healthcare systems

  • Weak AI training for nuanced clinical scenarios

SIGNALS MISSED

  • Reports from clinicians about irrelevant trial matches

  • Low adoption rates during pilot programs

HOW COULD THEY HAVE AVOIDED THIS

  • Improving data collection and standardization efforts

  • Incorporating clinician feedback into AI training

TEAMS INVOLVED

Data, Product, Engineering, Customer Success

Invitae Variant Interpretation Tool

BioTech

Invitae

The tool aimed to assist clinicians with genetic variant interpretation but failed due to poor usability and limited clinical validation.

WHAT WENT WRONG

  • Weak user interface design for clinicians

  • Insufficient integration with clinical workflows

SIGNALS MISSED

  • Low engagement rates among clinicians during trials

  • Feedback highlighting difficulty navigating the tool

HOW COULD THEY HAVE AVOIDED THIS

  • Conducting user-centered design sessions with clinicians

  • Validating workflows through real-world clinical testing

TEAMS INVOLVED

Product, Design, Engineering, QA, Customer Success

Edison Device Software

BioTech

Theranos

The software powering Theranos' blood-testing devices was criticized for producing inaccurate results, undermining the product’s credibility.

WHAT WENT WRONG

  • Poor algorithmic accuracy for test data processing

  • Lack of independent validation or peer-reviewed studies

SIGNALS MISSED

  • Early feedback from labs highlighting inconsistencies

  • Concerns from internal employees about data accuracy

HOW COULD THEY HAVE AVOIDED THIS

  • Conducting rigorous, third-party validation of the software

  • Engaging regulatory bodies earlier in the development process

TEAMS INVOLVED

Product, Engineering, QA, CEO, Compliance

Fractional Executives

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