Terminology And Classifications Delivery Service

[Crispina Blomker]

Throughoutthe Reference Guide, PPPs are described in terms of three broad parameters: first, the type of asset involved; second, what functions the private party is responsible for; and third, how the private party is paid.

A central characteristic of a PPP contract is that it bundles together multiple project phases or functions. Nonetheless, the functions for which the private party is responsible vary and depend on the type of asset and service involved. Typical functions include:

For the provision of these services, the private party typically creates a PPP company, a Special Purpose Vehicle (SPV). A dedicated SPV allows for the segregation of all assets and liabilities linked to the private provision of services.

The payment mechanism should be structured in such a way that the net remuneration of the private party is linked to performance. For the private party to have the right incentives to deliver services at the performance levels intended by the procuring authority, its remuneration, net of costs, should increase when approaching these levels. Additionally, sustained significant deviations from the intended performance levels should lead to contract cancellation, with termination payments designed so that quitting the project is never an easy solution for the private party.

This Reference Guide uses the term PPP to describe the wide range of contract types, regardless of the terminology in any specific country or jurisdiction. While PPP contracts can be categorized using the parameters above, there is no consistent, international standard for naming and describing these different types of contract. This varying terminology can create confusion when comparing international experience.

In some cases, PPPs are described by the functions transferred to the private party. For example, a Design-Build-Finance-Operate-Maintain, or DBFOM contract would allocate all those functions to the private party. Other nomenclatures such as Build-Operate-Transfer (BOT) focus instead on the legal ownership and control of the assets.

Under this nomenclature, the range of PPP contract types is described by the functions transferred to the private sector. The maintain function may be left out of the description (so instead of DBFOM, a contract transferring all those functions may simply be described as DBFO, with responsibility for maintenance implied as part of operations). An alternative description along similar lines is Design-Construct-Manage-Finance (DCMF), which is equivalent to a DBFOM contract.

The United Kingdom was one of the first countries to introduce the PPP concept under the term Private Finance Initiative, or PFI. It is typically used to describe a PPP as a way to finance, build and manage new infrastructure.

O&M contracts for existing assets may come under the definition of PPP where these are performance-based, long-term, and involve significant private investment (sometimes also called performance-based maintenance contracts).

The state retains asset ownership, and capital expenditure is the responsibility of the public sector, whereas operation and maintenance is the handled by the private sector. These types of contracts are 3-5 years in duration.

Coinciding with the ICD-11 2024 release, three new language versions of ICD-11 have been officially launched. 10 languages, Arabic, Chinese, Czech, English, French, Portuguese, Russian, Spanish, Turkish and Uzbek are available, with translations in an additional 25 languages underway.

The ICD-11 2024 version includes over 200 new codes for allergens, providing greater diagnostic detail and precision. In addition, digital tools and APIs have been significantly improved. This release includes the candidate version of the WHO Digital Open Rule Integrated Cause of Death Selection (DORIS) tool, now available in multiple languages, alongside updated APIs. This comprehensive upgrade is expected to improve and strengthen the quality of cause of death information across all member states, supporting better health data management and policy-making.

To facilitate the transition from ICD-10 to ICD-11, WHO has enhanced the digital mapping tables with additional mapping options, offering comprehensive cross-references and guides. These enhancements aim to ensure a smoother and more efficient migration process for all countries.

By 2024, the WHO has made significant progress in linking various medical classifications and terminologies to enhance global health interoperability. This includes lossless mapping of MedDRA (Medical Dictionary for Regulatory Activities) to facilitate accurate reporting of drug-related information, embedding medical device nomenclature for consistency across international health systems, and incorporating Orphanet terminology to improve the classification and understanding of rare diseases. Additionally, WHO is establishing approaches for technical collaboration and linkages with the MONDO Disease Ontology to support accurate disease classification, initiating design efforts with LOINC (Logical Observation Identifiers Names and Codes) to link laboratory and clinical observations with interventions, and exploring potential methods and frameworks for collaboration with other terminology systems to enhance comprehensive health information management.

ICD serves a broad range of uses globally and provides critical knowledge on the extent, causes and consequences of human disease and death worldwide via data that is reported and coded with the ICD. Clinical terms coded with ICD are the main basis for health recording and statistics on disease in primary, secondary and tertiary care, as well as on cause of death certificates. These data and statistics support payment systems, service planning, administration of quality and safety, and health services research. Diagnostic guidance linked to categories of ICD also standardizes data collection and enables large scale research.

For more than a century, the International Classification of Diseases (ICD) has been the basis for comparable statistics on causes of mortality and morbidity between places and over time. Originating in the 19th century, the latest version of the ICD, ICD-11, was adopted by the 72nd World Health Assembly in 2019 and came into effect on 1st January 2022.

Uses of the ICD are diverse and widespread and much of what is known about the extent, causes and consequences of human disease worldwide relies on use of data classified according to ICD. See below just a few examples:

Since the beginning of the pandemic and in response to member state requests, the classification and terminologies unit has been progressively activating emergency codes for COVID-19 in ICD-10 and ICD-11 after consultation with the relevant committees and reference groups of the WHO Family of International Classifications (WHO-FIC) Network.

The Delegates entrusted WHO, as one of its functions, with the task of establishing and revising the necessary international nomenclatures of diseases and causes of death, giving the WorldHealth Assembly authority to adapt regulations in respect, such as nomenclatures, for consideration and action, the International Statistical Classification ofDiseases, Injuries and Causes of Death and accompanying recommendations, destined to improve international uniformity and comparability of statistics of morbidity and mortality.

The UMLS integrates and distributes key terminology, classification and coding standards, and associated resources to promote creation of more effective and interoperable biomedical information systems and services, including electronic health records.

The UMLS, or Unified Medical Language System, is a set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems.

To install the UMLS on your computer, download the files. The MetamorphoSys tool, included with the downloaded files, allows you to customize the UMLS according to your needs. You can then load your customized data into your own database system, such as MySQL or Oracle, or you may browse your data using the MetamorphoSys RRF browser.

A technique for evaluating the importance of a featureor component by temporarily removing it from a model. You thenretrain the model without that feature or component, and if the retrained modelperforms significantly worse, then the removed feature or component waslikely important.

For example, suppose you train aclassification modelon 10 features and achieve 88% precision on thetest set. To check the importanceof the first feature, you can retrain the model using only the nine otherfeatures. If the retrained model performs significantly worse (for instance,55% precision), then the removed feature was probably important. Conversely,if the retrained model performs equally well, then that feature was probablynot that important.

A/B testing usually compares a single metric on two techniques;for example, how does model accuracy compare for twotechniques? However, A/B testing can also compare any finite number ofmetrics.

Accelerator chips (or just accelerators, for short) can significantlyincrease the speed and efficiency of training and inference taskscompared to a general-purpose CPU. They are ideal for trainingneural networks and similar computationally intensive tasks.

Binary classification provides specific namesfor the different categories of correct predictions andincorrect predictions. So, the accuracy formula for binary classificationis as follows:

Although a valuable metric for some situations, accuracy is highlymisleading for others. Notably, accuracy is usually a poor metricfor evaluating classification models that processclass-imbalanced datasets.

For example, suppose snow falls only 25 days per century in a certainsubtropical city. Since days without snow (the negative class) vastlyoutnumber days with snow (the positive class), the snow dataset forthis city is class-imbalanced.Imagine a binary classificationmodel that is supposed to predict either snow or no snow each day butsimply predicts "no snow" every day.This model is highly accurate but has no predictive power.The following table summarizes the results for a century of predictions:

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