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AwardedFind a Tender · award

1808 Risk Stratification Algorithms Tool for NHS AGEM CSU

Buyer: NHS Arden and GEM CSU →

BuyerNHS Arden and GEM CSU
StatusAwarded
DeadlineNot published
ValueValue not published
Published26 Nov 2024

What is being bought

NHS Arden & GEM CSU sought competitive offers for the supply, installation, support, and maintenance of Risk Stratification Algorithms within a Tool able to cover a population of 3.3 million patients.<br/><br/>The Risk Stratification Algorithms/Tool must meet the following key requirements:<br/>Have a proven evidence base and be rigorously tested using standardised statistical metrics and support repeatable results from the same data set.<br/>Be continually updated and supported to reflect changes in clinical practice and patient behaviour.<br/>The tool should have had experience of operating in the NHS and with associated NHS data flows or equivalent.<br/>Must be predicated on clinical evidence including a combination of prescription, diagnosis, and event data rather than purely historical financial spend in secondary care.<br/>Be able to utilise, as a minimum, acute, and primary care data records as a basis for its stratification<br/>Use multiple years of data to support a longitudinal record which can be updated on an automated basis by AGCSU.

Delivery location

UKF1

Categories

IT software development services 72212517IT software development services 72212517

Lot details

Lot 1

NHS Arden & GEM CSU sought competitive offers for the supply, installation, support, and maintenance of Risk Stratification Algorithms within a Tool able to cover a population of 3.3 million patients.<br/><br/>The Risk Stratification Algorithms/Tool must meet the following key requirements:<br/>Have a proven evidence base and be rigorously tested using standardised statistical metrics and support repeatable results from the same data set.<br/>Be continually updated and supported to reflect changes in clinical practice and patient behaviour.<br/>The tool should have had experience of operating in the NHS and with associated NHS data flows or equivalent.<br/>Must be predicated on clinical evidence including a combination of prescription, diagnosis, and event data rather than purely historical financial spend in secondary care.<br/>Be able to utilise, as a minimum, acute, and primary care data records as a basis for its stratification<br/>Use multiple years of data to support a longitudinal record which can be updated on an automated basis by AGCSU.<br/>The Risk Stratification Tool must have a range of predictive models with ability to include as a minimum:<br/>Current and predicted costs.<br/>Predicted resource utilisation.<br/>Risk of hospitalisation.<br/>The algorithms within the tool must be able to factor in sufficient historical data to enable the clinical evidence-base of the tool, including historical diagnosis of long-term conditions and support and provide disease profiling. It should capture the multidimensional nature of an individual’s health.<br/>The Risk Stratification algorithms must be able to be housed and run within the AGCSU data management environment to maintain our data controls and governance and allow it to be augmented by other data elements managed by the customer. All outputs of the tool must be programmatically readable, must output validation to measure success of the processing and use a server-based technology not a desktop to enable flexible and secure working.

Statuscancelled

Award criteria
price

What is included

ItemCategoryQuantity
1IT software development servicesNot published

Comparable-procurement analytics

Benchmarked against retained Find a Tender procedures with CPV division 72. The category anchor is IT software development services (72212517); this is a deliberately broad market comparator. The comparison is shown at several levels rather than pretending one company or region is always the best benchmark.

Comparison setProceduresReported bids per procedureNamed award suppliersPrice evidence
Market: CPV division 7210,5392 median · 15.4 average (4,161 of 10,539 with a bid count)2 average (5,053 of 10,539 with named award suppliers)Not published
Same buyer14 median · 4 average (1 of 1 with a bid count)1 average (1 of 1 with named award suppliers)Not published
Delivery region: UKF1533 median · 3.3 average (21 of 53 with a bid count)1 average (23 of 53 with named award suppliers)Not published

“Reported bids” is an official aggregate, sometimes reported per lot; it is the closest available competition measure. “Named award suppliers” are winners, not all applicants.

Price-outcome signal

Not enough comparable procedures currently publish both a GBP tender value and a usable lowest-valid-bid value to calculate a responsible price-reduction benchmark. Tenderline deliberately does not infer a saving from named award suppliers or from missing award values.

Procurement strategy & market signals

Framework agreementNot published
Dynamic purchasing systemNot published
Competitive procurementNot published
Recurring requirementNot published
Procurement method rationaleNot published
Rationale classificationsNot published
Special regimeNot published
Covered byGPA
Submission policyNot published
Selection criteriaNot published
Risk detailsNot published

Planning & early market engagement

BudgetNot published
No-engagement rationaleNot published
Planning documents0
Planning milestones0

No planning milestones published.

Related procurements

No linked framework, prior procurement or reprocurement published.

Awards

Contracts

038164-2024-1

Statusactive
Value£188,400

Documents & submission route

No documents are published in the current source record.

Source data inventory

Diagnostic view. “Not published” means this current release does not provide a value.

OCIDocds-h6vhtk-0458e8
Latest release ID038164-2024
Latest release timestampTue Nov 26 2024 13:55:25 GMT+0000 (Coordinated Universal Time)
Sourcefind-a-tender
Official notice URLNot published
Tender statuscomplete
Procurement methodselective
Procurement method detailsCompetitive procedure with negotiation
Main procurement categoryservices
Above thresholdNot published
Legal basis32014L0024
Tender period: startNot published
Tender period: endNot published
Expression of interest deadlineNot published
Enquiry deadlineNot published
Award period: startNot published
Award period: endNot published
Submission method detailsNot published
Submission languagesNot published
Electronic catalogue policyNot published
Total tender valueNot published
Tender lots in source1
Tender items in source1
Tender documents in source0
Awards in latest release1
Contracts in latest release1
Parties in latest release3

Notice history

DateEventReference
26 Nov 2024award, contract038164-2024
21 Nov 2024award, contract037619-2024
10 May 2024tender015058-2024

All source data

Unmodified official OCDS data retained by Tenderline for this procurement process.

Complete current OCDS release JSON
{
  "id": "038164-2024",
  "tag": [
    "award",
    "contract"
  ],
  "bids": {
    "statistics": [
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  "date": "2024-11-26T13:55:25Z",
  "ocid": "ocds-h6vhtk-0458e8",
  "buyer": {
    "id": "GB-FTS-95447",
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  },
  "awards": [
    {
      "id": "038164-2024-1",
      "status": "active",
      "suppliers": [
        {
          "id": "GB-FTS-131293",
          "name": "Johns Hopkins HealthCare LLC"
        }
      ],
      "relatedLots": [
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  ],
  "tender": {
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    "title": "1808 Risk Stratification Algorithms Tool for NHS AGEM CSU",
    "status": "complete",
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    "classification": {
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    "procurementMethod": "selective",
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  "parties": [
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        "countryName": "United Kingdom",
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      },
      "details": {
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      "identifier": {
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    },
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      "id": "GB-FTS-131293",
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      "roles": [
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      "address": {
        "region": "US",
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        "countryName": "United States",
        "streetAddress": "7231 Parkway Drive, Suite 100"
      },
      "details": {
        "scale": "large"
      },
      "identifier": {
        "legalName": "Johns Hopkins HealthCare LLC"
      }
    },
    {
      "id": "GB-FTS-1661",
      "name": "The High Court",
      "roles": [
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      ],
      "address": {
        "locality": "London",
        "postalCode": "WC2A 2LL",
        "countryName": "United Kingdom",
        "streetAddress": "The Strand"
      },
      "details": {
        "url": "https://www.judiciary.uk/courts-and-tribunals/high-court/"
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      "identifier": {
        "legalName": "The High Court"
      }
    }
  ],
  "language": "en",
  "contracts": [
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      "value": {
        "amount": 188400,
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      "status": "active",
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      "dateSigned": "2024-11-15T00:00:00Z"
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  ],
  "description": "This service has been awarded using Regulation 32(2)(a) of the negotiated procedure without prior publication, in line with the PCR15 Regulations; following a non-award of contract ref: (2024/S 000-015058).<br/><br/>The successful provider did not meet the advertised Social Value requirement of the initial process advertised and following clarification and assurance from the provider of their ability, has been awarded the contract under Regulation 32.",
  "initiationType": "tender"
}
Complete JSON history (3 releases)
26 Nov 2024 · 038164-2024 · award, contract
{
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      "relatedLots": [
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  ],
  "tender": {
    "id": "C321747",
    "lots": [
      {
        "id": "1",
        "status": "cancelled",
        "hasOptions": false,
        "description": "NHS Arden & GEM CSU sought competitive offers for the supply, installation, support, and maintenance of Risk Stratification Algorithms within a Tool able to cover a population of 3.3 million patients.<br/><br/>The Risk Stratification Algorithms/Tool must meet the following key requirements:<br/>Have a proven evidence base and be rigorously tested using standardised statistical metrics and support repeatable results from the same data set.<br/>Be continually updated and supported to reflect changes in clinical practice and patient behaviour.<br/>The tool should have had experience of operating in the NHS and with associated NHS data flows or equivalent.<br/>Must be predicated on clinical evidence including a combination of prescription, diagnosis, and event data rather than purely historical financial spend in secondary care.<br/>Be able to utilise, as a minimum, acute, and primary care data records as a basis for its stratification<br/>Use multiple years of data to support a longitudinal record which can be updated on an automated basis by AGCSU.<br/>The Risk Stratification Tool must have a range of predictive models with ability to include as a minimum:<br/>Current and predicted costs.<br/>Predicted resource utilisation.<br/>Risk of hospitalisation.<br/>The algorithms within the tool must be able to factor in sufficient historical data to enable the clinical evidence-base of the tool, including historical diagnosis of long-term conditions and support and provide disease profiling. It should capture the multidimensional nature of an individual’s health.<br/>The Risk Stratification algorithms must be able to be housed and run within the AGCSU data management environment to maintain our data controls and governance and allow it to be augmented by other data elements managed by the customer. All outputs of the tool must be programmatically readable, must output validation to measure success of the processing and use a server-based technology not a desktop to enable flexible and secure working.",
        "awardCriteria": {
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    "status": "complete",
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21 Nov 2024 · 037619-2024 · award, contract
{
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  "date": "2024-11-21T10:10:06Z",
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        "description": "NHS Arden & GEM CSU seeks competitive offers for the supply, installation, support, and maintenance of Risk Stratification Algorithms within a Tool able to cover a population of 3.3 million patients.<br/><br/>The Risk Stratification Algorithms/Tool must meet the following key requirements:<br/>Have a proven evidence base and be rigorously tested using standardised statistical metrics and support repeatable results from the same data set.<br/>Be continually updated and supported to reflect changes in clinical practice and patient behaviour.<br/>The tool should have had experience of operating in the NHS and with associated NHS data flows or equivalent.<br/>Must be predicated on clinical evidence including a combination of prescription, diagnosis, and event data rather than purely historical financial spend in secondary care.<br/>Be able to utilise, as a minimum, acute, and primary care data records as a basis for its stratification<br/>Use multiple years of data to support a longitudinal record which can be updated on an automated basis by AGCSU.<br/>The Risk Stratification Tool must have a range of predictive models with ability to include as a minimum:<br/>Current and predicted costs.<br/>Predicted resource utilisation.<br/>Risk of hospitalisation.<br/>The algorithms within the tool must be able to factor in sufficient historical data to enable the clinical evidence-base of the tool, including historical diagnosis of long-term conditions and support and provide disease profiling. It should capture the multidimensional nature of an individual’s health.<br/>The Risk Stratification algorithms must be able to be housed and run within the AGCSU data management environment to maintain our data controls and governance and allow it to be augmented by other data elements managed by the customer. All outputs of the tool must be programmatically readable, must output validation to measure success of the processing and use a server-based technology not a desktop to enable flexible and secure working.",
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    ],
    "title": "1771 Provision of Risk Stratification Algorithms Tool For NHS Arden and GEM CSU",
    "status": "unsuccessful",
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    "procurementMethodDetails": "Open procedure"
  },
  "parties": [
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      },
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      "contactPoint": {
        "name": "Mark Didcock",
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      }
    },
    {
      "id": "GB-FTS-1661",
      "name": "The High Court",
      "roles": [
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      ],
      "address": {
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        "countryName": "United Kingdom",
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      "details": {
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        "legalName": "The High Court"
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  ],
  "language": "en",
  "description": "Due to receiving no compliant bids, this process has been concluded as a non award.",
  "initiationType": "tender"
}
10 May 2024 · 015058-2024 · tender
{
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  "date": "2024-05-10T16:34:53+01:00",
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