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Build the Algorithm or Buy the Platform: How a Hospital Network Used Bespoke Research and Strategy Advisory to Decide Which Analytics Capabilities to Develop In-House

Build the Algorithm or Buy the Platform: How a Hospital Network Used Bespoke Research and Strategy Advisory to Decide Which Analytics Capabilities to Develop In-House

Executive Snapshot

Client

Regional Hospital Network, Texas

Situation/Challenge

The client's data and analytics team had been fielding requests from clinical, operational, and financial leadership for predictive and prescriptive analytics capabilities across a range of use cases, from readmission risk scoring to theatre utilisation optimisation. The team had the data science capability to build models in-house but not the bandwidth to build everything simultaneously. Simultaneously, several analytics vendors were pitching pre-built solutions for many of the same use cases. The analytics director needed a principled framework for deciding which use cases to build internally and which to buy.

Objective

Commission bespoke research into the healthcare analytics market to establish which analytics use cases were genuinely differentiating when built in-house versus which were commoditised and better sourced from vendors, then engage strategy advisory to apply the research findings to the client's specific use case backlog and data environment.

Constancy Researchers Solution

Customized Research combined with Strategy & Growth Advisory, bespoke research into healthcare analytics build-versus-buy dynamics by use case category, followed by strategy advisory applying the research to the client's use case backlog and internal data science capacity.

Impact

Bespoke research identified that readmission risk scoring and discharge planning optimisation had become sufficiently commoditised in the vendor market that in-house build offered limited additional clinical value over well-implemented vendor solutions. Theatre utilisation and surgeon scheduling optimisation, by contrast, required the kind of institution-specific historical data and workflow integration that vendor solutions consistently failed to replicate at the level of performance the client's surgeons and theatre managers would accept.

Client Outcome

The client built its theatre utilisation and scheduling models in-house while purchasing vendor solutions for readmission risk and discharge planning, concentrating data science capacity on the use cases where institutional specificity produced genuine performance advantage.

The Situation / Challenge

Healthcare analytics has matured to a point where a hospital network faces genuinely different build-versus-buy economics depending on the specific use case in question. For some clinical and operational analytics applications, the vendor market has produced well-validated solutions trained on large multi-institution datasets that outperform what a single hospital network’s data science team could build in a reasonable timeframe.

The client’s analytics team had accumulated a backlog of twenty-two analytics use case requests from across the network and was trying to decide how to sequence and source them without a principled framework for making those decisions. The data science team was capable of building sophisticated models but was expensive and limited in bandwidth.

The result was a use case backlog that was growing faster than the team’s capacity to address it, a set of vendor evaluations being run without a clear standard for what the vendor needed to demonstrate to beat an in-house build, and a data science team increasingly uncertain about what the organisation was actually expecting them to own.

Key Challenges

  • No bespoke research into which healthcare analytics use cases had become sufficiently commoditised enough that vendor solutions offered comparable performance to an in-house build.
  • No framework for deciding which use cases required institution-specific historical data and workflow integration that vendor solutions were unlikely to match.
  • A use case backlog of twenty-two requests growing faster than the data science team’s capacity to address them.
  • Vendor evaluations run without a clear standard for what a vendor solution needed to demonstrate to beat an in-house alternative.
  • Data science team bandwidth being consumed by use cases that might have been better sourced from vendors.
  • Analytics director pressure to produce a principled framework presentable to clinical, operational, and financial leadership as a coherent strategy.

Not all healthcare analytics use cases are equally served by in-house build, and not all are equally served by vendor solutions. The distinction turns on whether the performance of the model depends primarily on the size and diversity of the training dataset, which favours vendor solutions, or on the granularity and specificity of the institution’s own historical operational data, which favours in-house development.

Constancy Researchers Solution

Constancy Researchers designed the engagement to produce both a market-grounded research basis for the build-versus-buy distinction and a specific application of that distinction to the client’s own use case backlog, so the analytics director could present a defensible resource allocation to leadership rather than a queue ordered by request volume.

Healthcare Analytics Market: Build-vs-Buy Dynamics Research
  • Delivered bespoke research into the healthcare analytics vendor market across the use case categories represented in the client’s backlog.
  • Identified two clusters: commoditised use cases where vendor solutions matched or exceeded in-house performance, and institution-specific use cases where generic vendor models failed to gain clinical adoption.
Use Case Commoditisation Assessment
  • Assessed each of the client’s twenty-two use case requests against the commoditisation dimension.
  • Found that twelve use cases, including readmission risk scoring, length-of-stay prediction, and discharge planning optimisation.
Institution-Specific Use Case Identification
  • Identified ten institution-specific use cases, including theatre utilisation, surgeon scheduling, and staffing prediction, where the client’s own historical data was the primary performance driver.
  • Confirmed through consulting analysis that each of the ten institution-specific use cases depended on the granularity of the client’s own historical scheduling.
Data Science Capacity Allocation & Sequencing
  • Delivered a capacity allocation recommendation concentrating the data science team on the ten institution-specific use cases in priority order.
  • Sequenced the institution-specific in-house builds to begin with theatre utilisation and surgeon scheduling.
Vendor Evaluation Standard & Governance Framework
  • Delivered a vendor evaluation standard for the commoditised use cases, defining the specific clinical validation evidence, integration pathway.

The engagement gave the analytics director a resource allocation strategy built on a principled distinction between use cases the team should own and those where the vendor market had already done the hard work, rather than a queue managed by the volume and seniority of internal requests.

Impact

  • Bespoke research identified two distinct clusters across the use case landscape, commoditised and institution-specific, with different build-versus-buy implications.
  • Twelve of the twenty-two backlog use cases were confirmed as commoditised, with vendor solutions offering comparable or superior performance to an in-house build.
  • Ten use cases were confirmed as institution-specific, where vendor solutions were unlikely to achieve the clinical adoption the client required.
  • Data science capacity was concentrated on the ten institution-specific use cases in priority sequence.
  • Theatre utilisation and surgeon scheduling were prioritised first, addressing the longest-standing clinical leadership requests.
  • A structured vendor evaluation was initiated for the twelve commoditised use cases against a defined performance standard.
  • Readmission risk scoring and discharge planning optimisation were sourced from vendor solutions, freeing data science capacity for institution-specific builds.
  • The analytics director presented a coherent, principled resource allocation to clinical, operational, and financial leadership.

Client Outcome

Resource Allocation

Data science capacity was concentrated on institution-specific use cases, freeing bandwidth previously consumed by commoditised applications vendor solutions could address.

Clinical Prioritisation

Theatre utilisation and surgeon scheduling were delivered first, addressing the clinical leadership requests with the longest wait and generating internal advocacy for the broader programme.

Vendor Sourcing

Readmission risk scoring and discharge planning were sourced from validated vendor solutions, delivering faster time-to-value on commoditised use cases.

Backlog Clarity

A principled framework replaced a queue managed by request volume and requester seniority, giving the analytics team a defensible basis for sequencing decisions.

Vendor Standard Defined

A performance benchmark and clinical validation standard gave the evaluation team an objective basis for vendor selection on commoditised use cases.

Leadership Alignment

Clinical, operational, and financial leadership aligned around a shared resource allocation strategy rather than continuing to compete for analytics team bandwidth.

Team Focus

The data science team gained clarity about which use cases the organisation expected them to own and which were appropriate for vendor sourcing.

Capacity Protection

In-house build capacity was protected for the use cases where institutional specificity produced genuine performance advantage over the vendor market.

Market Positioning

The hospital network was repositioned as an analytics organisation that builds where it has a data advantage and buys where the vendor market has already solved the problem.

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