003-Inside the Mission: A Behind-the-Scenes Look at Healthcare Innovation in a Mission-Led Approach

Mission Reflections 003: Houston! Are we ready? System Readiness: Assessing the Data Infrastructure

During the Apollo 11 mission, Neil Armstrong (Mission Commander) and Buzz Aldrin (Lunar Module Pilot) made a last-minute decision to manually land the lunar module, Eagle. As they were descending, the onboard computer was guiding them towards a rugged, boulder-filled area, which was unsafe and miles away from the planned landing location. Armstrong took manual control to steer the module to a safer spot, ultimately landing Eagle safely. This mission-saving intervention was possible because of a robust mission data infrastructure, which provided real-time telemetry and actionable insights to the astronauts.

In the first two blogs of this series, we explored the early stages of launching a mission-led approach in healthcare, focusing on defining a problem and a clear vision of success. The following posts will shift attention to a critical yet often overlooked aspect: system readiness. Without a prepared ecosystem, even the most innovative missions can struggle to take off. This blog will specifically focus on assessing the readiness of the data infrastructure.

Is the System Ready for Transformation?

Launching a mission isn’t as simple as setting a goal and pressing forward. Much like Apollo 11’s astronauts relied on a robust pre-launch data infrastructure to select a landing location and track real-time transit data via telemetry, any healthcare mission must first assess whether the system's data infrastructure can support the ambitious goals ahead.

In the context of improving mental health outcomes for Children and Young People (CYP), the stakes are high: reducing by 25% the number of children and young people presenting in mental health crises at Accident & Emergency (A&E) departments. Anecdotal evidence suggested that Looked After Children and CYP with Neurodevelopmental Disorders (NDD) were disproportionately represented in the cohort of CYP presenting in crisis at A&E. Even the most groundbreaking ideas will fail if the system is not structurally prepared to measure and track the impact of innovations designed to achieve better outcomes in these areas. This means solutions are likely to sit at the interface between health and social care data and our understanding of NDD services.

Assessing Data Infrastructure: Building the Foundation

One of the most critical steps in assessing system readiness is evaluating the data infrastructure. Data is the lifeblood of any healthcare mission—without reliable, accurate, and timely data, it’s impossible to measure progress or understand impact. In our mission, one of the first questions we asked was, “What’s the state of our data?”

This assessment goes beyond simply asking whether data is being collected. We needed to explore deeper questions: Is the data consistent across different hospital A&Es, Mental Health Trusts, GP practices, and other care settings? Are all A&Es using the same criteria to define and record a mental health crisis? Are all Neurodevelopmental Disorder services collecting data consistently in a standard format? More importantly, can we track the journey of a CYP across the entire health and social care system?

Although I consider the mission I led to be located in a health system with one of the most advanced data infrastructures in the country (certainly the envy of many), my mission experience also highlighted areas for development. For colleagues embarking on a similar mission in CYP Mental Health, here are reflections on what a functional data infrastructure might look like:

? Mental Health data, such as the Mental Health Service Data Sets (MHSDS), from trusts needs to be linked with other datasets, such as those from primary care and acute settings, to enable tracking of children’s interactions across different services.

? Acute data should have consistent coding, and A&E datasets and data collection need to be standardised for accurate trend analysis over time.

? Children’s social care and health data should be integrated to confirm if children attending A&E are known to social services, enabling a more holistic understanding of their circumstances.

? Neurodevelopmental Disorder services require streamlined data collection and coding to reduce fragmentation and ensure consistent updates.

? Existing system transformation initiatives should be tracked comprehensively to ensure measurement of both reach and impact.

A complete and integrated data approach is necessary to develop evidence-based solutions that enhance the effectiveness of any mission. Much like the importance of precise telemetry in Apollo 11, the success of healthcare missions depends on accurate and comprehensive data. Gaps in data infrastructure are akin to attempting a Moon landing without a complete map of the lunar surface—it’s a risky venture.

The Role of Qualitative Data

Practically, the focus above is on quantitative data. However, quantitative data is not always of the quality needed to answer the questions posed by a mission. When gaps arise, qualitative insights can serve as an invaluable tool. Engaging with clinicians, programme leaders, young people, and their families through interviews and focus groups can provide meaningful context when precise numerical data is lacking. On the lunar mission, Aldrin and Armstrong had to rely not just on instruments, but on their judgement and human insight as they manually piloted the module to a safe landing. Similarly, when quantitative data is incomplete, healthcare professionals must rely on qualitative insights to guide critical decisions.

Exploring the theme of creatively working with suboptimal data within a mission context is a topic deserving of future exploration.

Key Lessons on System Readiness and Data Infrastructure

Here are some critical lessons from my healthcare mission journey, relevant to anyone working in mission-led approaches:

1. Assess the system’s readiness before launching the mission. Challenge any assumptions that a “mission” is always the best option. While missions offer excitement, they may not be suitable at every stage of a system’s development.

2. Understand the system’s data capabilities. Examine what data exists and whether it can support the mission’s goals. Identify baseline data on critical metrics and evaluate its quality.

3. Recognise the limitations of the data. Data inconsistencies between organisations can make aggregation difficult. Assess gaps, poor-quality data, and non-standardised data collection methods.

4. Be aware of hidden data inconsistencies. Data inconsistencies may be hidden in the configuration of services. For example, Neurodevelopmental Disorder services may be configured differently across geographies, making it difficult to aggregate even good-quality data into a system-wide picture.

5. Plan for data gaps. When comprehensive quantitative data is unavailable, qualitative methods such as expert interviews or focus groups with clinicians, programme leaders, and service users can help fill the gaps.

6. Use proxies to measure impact. When data is imperfect, use proxies or innovative measurement approaches to ensure progress is tracked effectively.

7. Avoid the “attribution hold-up.” Stakeholders may have a vested interest in historical data questions, such as which previous programs contributed most to a system’s current state. Manage these concerns without allowing them to derail the mission.

8. Balance detailed analysis with pragmatism. Not all missions require exhaustive data reviews. Focus on what’s most critical for achieving success rather than getting bogged down in endless analysis.

Conclusion: The Importance of Readiness

The success of a healthcare mission depends on thorough preparation, and assessing system readiness is a non-negotiable step in this process. As we’ve learned from working to improve CYP mental health outcomes, missions require more than just great ideas and innovative tools—they need a data infrastructure capable of supporting the innovations and tracking their impact. By ensuring the system is prepared for transformation, we can lay the groundwork for meaningful and lasting change. Only then, like the crew of Apollo 11, can we take that giant leap forward.

Stay tuned for my next post, where we’ll explore another key element of system readiness and how to drive successful outcomes.

Particular thanks to colleagues Benjamin Pierce Joana Flores Matthew Chisambi Andi Orlowski Axel Heitmueller Heather Fitzke who contributed directly to the data analysis referenced in this blog or have shaped my understanding of healthcare data over time.

Larry Koyama Lydia Salice Joana Flores Fazeela Chharawala Matthew Chisambi Benjamin Pierce Axel Heitmueller Morenike Ajayi MBE DStg, MBA, FCPFA, Dr T.Ayodele Ajayi FRCPsych Kemi Nelson Tunde Fasoyiro Ayobami Popoola Funke Sadare Chartered FCIPD, MA in HRM Craig Brown Danny Bosch, PhD FRSA Chris Dewhurst Chris Marot Dr Chris Russell NHS Leadership Academy ?? Richard Stubbs Scott Thowney Sarah Helps Marsha David Alan Davies Jay Dungeni Tommy Fuller Finn McNulty Yusuf Shittu MD, MSc Gift Pola Kiti, MPH Temitayo Johnson-Laleye, ACIPM Bharti Patel Jaime Squire-Dean Carly Snowball Sally Vaughan Bernadette Thompson OBE FCIPD Tony Mears Andi Orlowski Tony Roberts Shirlene Oh Yinka Makinde Alice Henderson Tom Clutterbuck Shakti Dookeran Matthew Wyatt Clare Thomson Adam Ashworth Kemi Bolarin Omorinola (Morin) Carew MBA Dr Maria Ilia James Plunkett Femi Bolaji Deji Lewis Marianne Lewis Rebecca Whitting Andrew George Shirlene Oh Jack Howell Clarerita (Rita) Higgins Tahseen Tayob MD MPH Hilary Scott Rebekah Tailor Chart.PR MCIPR Katie Harrison Harry Canagasabey Kevin Hunter Jack Howell Andrew George Dr Chris Russell Matt Inada-Kim Robert McLean Charles Conn Jonathon Gray Jon Siddall Sadie Myhill Emma Dabbs Emma Sharpe Katie Harrison Professor Bryan Williams OBE MD FMedSci Professor Derek Bell Prof. Bola Owolabi, MRCGP MFPH(Hon), FRSPH Dr Simisola Alabi MBBS MRCGP MSc Victor Odunaiya Dr Sheena Visram Ph.D BABATUNDE G OGUNDUNMADE PT Melissa Rochon Kate Hall Kate Cheema Kate Emery Greg Quinn Logan Ryan Deborah Harrison Eamonn Lyster Dr. Esther Gathogo, MPharm, PhD Kevin Fong Joy Tweed Alan Davies Henry Ireland Sara Rankin