Horizon scanning provides timely intelligence about innovative health technologies in clinical development by commercial and non-commercial organizations. The horizon scanning for obesity medicines, carried out by the National Institute for Health and Care Research Innovation Observatory (IO), aimed to identify emerging obesity medicines to inform decision-making by national stakeholders and to shape future research.

In July 2023, the IO utilized horizon scanning methodology to identify medicines for preventing and treating obesity either primarily or as a comorbidity. The scans included medicines in preclinical and clinical development (phase I, I/II, II, II/III, III, or IV) sponsored by industry and non-industry for all population groups. Trial locations included Australia, Canada, the European Union, the UK, and the USA. Data were collected from the IO’s internal database (the Medicines Innovation Database), ClinicalTrials.gov, the European Union Drug Regulating Authorities Clinical Trials Database, the World Health Organization International Clinical Trials Registry Platform, and the Citeline Pharmaprojects database. The data were systematically screened and analyzed.

A total of 405 clinical trials were identified that evaluated 177 unique medicinal interventions. Among these, 47 unique preclinical interventions were identified from preclinical studies. A total of 256 (63%) trials were sponsored by industry, 139 (34%) by non-industry, and 10 (3%) by industry and non-industry jointly. The top five drug classes included anorectic or anti-obesity medicines (n=75; 42%), antihyperglycemics (n=24; 14%), anti-inflammatories (n=8; 5%), hepatoprotectants (n=7; 4%), and antihyperlipidemics (n=4; 2%). At the time of scanning, 48 (27%) medicines were unlicensed in the UK and 129 (73%) were not. Among the licensed medicines, 37 (77%) were off patent and 11 (23%) were on patent.

The IO’s horizon scanning process can identify and deliver timely intelligence to support decision-making and facilitate adoption of new medicines to target areas of unmet clinical need. The obesity medicines scan identified medicinal interventions in preclinical and clinical development and provides valuable insights into the trends and research gaps in preventing and treating obesity.

Technology is central in supporting older people with their daily tasks and independence at home. This project aimed to identify technologies that can be built into residential environments (e.g., appliances, fixtures, or fittings) to support older people in activities of daily living (ADL) through a horizon scan (HS) informed by public insights on unmet needs and priorities.

A survey of members of the public was conducted to prioritize outcomes included within an evidence and gap map (EGM) framework. The EGM aimed to illustrate the current landscape of technologies supporting ADL in residential settings (e.g., care homes) and innovation gaps. The EGM results were shared with end users in a workshop discussion on the current range of technologies aimed at supporting ADL in residential settings. This was facilitated using vignettes to elicit views on unmet needs and priorities for technology development. The workshop informed the scope of the HS to identify and prioritize emerging technologies that could address unmet needs.

This project successfully embedded public involvement throughout to identify innovation gaps in technologies supporting ADL, unmet needs among end users, and potential solutions to these needs. The HS identified 190 technologies that were ready to market. All the technologies had potential to address identified unmet needs and could be built into the residential environment to support older people with ADL and to improve their quality of life, independence, and safety at home. Horizon scanning research can meaningfully involve stakeholders and take direction from their insights to enable voices less often heard to drive innovation in areas where it is needed.

Involving stakeholders in research using evidence synthesis and qualitative methods helps to gain a better understanding of gaps in innovation, the related unmet needs, and the technologies that might address these needs. Public involvement in the survey and workshop influenced the conduct and interpretation of the EGM, the scope of the HS, and the interpretation of the findings.

We developed a real-world evidence (RWE) based Markov model to project the 10-year cost of care for patients with depression from the public payer’s perspective to inform early policy and resource planning in Hong Kong.

The model considered treatment-resistant depression (TRD) and development of comorbidities along the disease course. The outcomes included costs for all-cause and psychiatric care. From our territory-wide electronic medical records, we identified 25,190 patients with newly diagnosed depression during the period from 2014 to 2016, with follow-up until December 2020 for real-world time-to-event patterns. Costs and time varying transition inputs were derived using negative binomial and parametric survival modeling. The model is available as a closed cohort, which studies a fixed cohort of incident patients, or an open cohort that introduces new patients every year. Utilities values and the number of incident cases per year were derived from published sources.

There were 9,217 new patients with depression in 2023. Our closed cohort model projected that the cumulative cost of all-cause and psychiatric care for these patients would reach USD309 million and USD58 million by 2032, respectively. In our open cohort model, 55,849 to 57,896 active prevalent cases would cost more than USD322 million and USD61 million annually in all-cause and psychiatric care, respectively. Although less than 20 percent of patients would develop TRD or its associated comorbidities, they contribute 31 to 54 percent of the costs. The key cost drivers were the number of annual incident cases and the probability of developing TRD and associated comorbidities and of becoming a low-intensity service user. These factors are relevant to early disease stages.

A small proportion of patients with depression develop TRD, but they contribute to a high proportion of the care costs. Our projection also demonstrates the application of RWE to model the long-term costs of care, which can aid policymakers in anticipating foreseeable burden and undertaking budget planning to prepare for future care needs.

It is vital that horizon scanning organizations can capture and disseminate intelligence on new and repurposed medicines in clinical development. To our knowledge, there are no standardized classification systems to capture this intelligence. This study aims to create a novel classification system to allow new and repurposed medicines horizon scanning intelligence to be disseminated to healthcare organizations.

A multidisciplinary working group undertook literature searching and an iterative, three-stage piloting process to build consensus on a classification system. Supplementary data collection was carried out to facilitate the implementation and validation of the system on the National Institute of Health and Care Research (NIHR) Innovation Observatory (IO)‘s horizon scanning database, the Medicines Innovation Database (MInD).

Our piloting process highlighted important issues such as the patency and regulatory approval status of individual medicines and how combination therapies interact with these characteristics. We created a classification system with six values (New Technology, Repurposed Technology (Off-patent/Generic), Repurposed Technology (On-patent/Branded), Repurposed Technology (Never commercialised), New + Repurposed Technology (Combinations-only), Repurposed Technology (Combinations-only)) that account for these characteristics to provide novel horizon scanning insights. We validated our system through application to over 20,000 technology records on the MInD.

Our system provides the opportunity to deliver concise yet informative intelligence to healthcare organizations and those studying the clinical development landscape of medicines. Inbuilt flexibility and the use of publicly available data sources ensure that it can be utilized by all, regardless of location or resource availability.

Horizon scanning for health technology appraisal (HTA) in England involves topic notification to the National Institute for Health and Care Excellence (NICE) via technology briefings. This activity is undertaken by the Innovation Observatory with submission timelines designed to ensure that HTA decisions align with regulatory approval time. In this paper, we aimed to track and assess the progression and current status of the topics notified for HTA and provide a descriptive analysis of these topics.

Technology briefings were mapped from submission to NICE technology appraisal/highly specialized technologies recommendations from April 2017 until October 2021. This was done using a combination of searches on Google and NICE website, searching a downloadable spreadsheet containing NICE topic selection decisions, and querying NICE Topic Selection team. Analysis was undertaken regarding type of indications and interventions of submitted topics and published guidance.

Six-hundred and ninety-three topics entered the NICE scoping process, of which 94 percent were prioritized. As of November 2021, approximately 39 percent of prioritized topics were in scoping/in progress, 31 percent were proposed/completed, 20 percent were suspended/terminated, and 4 percent were referred back to Innovation Observatory (IO) for further monitoring.

Our work demonstrates that horizon scanning for HTA is a complex and time-intensive process. Timelines and progress through HTA is challenging due to the growing number of innovative medicines, significant uncertainties, and limited transparency in clinical development and regulatory pathways. A better understanding of clinical trials and regulatory requirements may help eliminate some of this uncertainty and improve timely HTA.

In recent years, there has been a growing recognition that health equity and health inequalities should be a consideration in all aspects of research. Since the Commission on Social Determinants of Health by the World Health Organization was established in 2005, there has been a growing interest in tackling systemic differences in health outcomes, including expanding the scope to health research including evidence synthesis and health technology assessments (HTA). This analysis aims to identify health inequality and health inequity frameworks that exist to help structure and plan research methods in evidence synthesis.

A critical analysis of the existing frameworks used in evidence synthesis to address health inequality and/or inequity was undertaken. Comprehensive, systematic searching of seven social science electronic databases and grey literature was undertaken based on the Behavior/phenomenon of interest, Health context and Model/Theory (BeHEMoTh) model, from 1990 to May 2022 to identify all relevant studies. A narrative synthesis approach was used to critically appraise the existing frameworks.

Sixty-two reviews published between 2008 and 2022 reporting on using a framework to stratify health opportunities and outcomes met the inclusion criteria. Frameworks identified included the PROGRESS (place of residence, race or ethnicity, occupation, gender, religion, educational level, socioeconomic status, and social capital), PROGRESS-Plus (plus age, disability and sexual orientation) and Preferred Reporting Items for Systematic Reviews and Mata Analysis (PRISMA) – Equity checklist.

Currently, there does not seem to be consensus in how evidence of inequality or inequity in evidence synthesis or HTA are reported. As research interests in health inequality and inequity continue to grow, there is a need to develop a framework that provides an in-depth understanding of how inequalities in health and inequities in health should be considered within evidence synthesis and HTA. This will allow researchers to analyze not just the effects of interventions, but also how healthcare outcomes are impacted by inequalities or inequities.

In areas where public confidence is low and there is a lack of understanding around behaviors, such as COVID-19 vaccine hesitancy, there is a need to explore novel sources of evidence. When leveraged using artificial intelligence (AI) techniques, social media data may offer rich insights into public concerns around vaccination. Currently, sources of ‘soft-intelligence’ are underutilized by policy makers, health technology assessment (HTA) and other public health research agencies. In this work, we used an AI platform to rapidly detect and analyze key barriers to vaccine uptake from a sample of geo-located tweets.

An AI-based tool was deployed using a robust search strategy to capture tweets associated with COVID-19 vaccination, posted from users in London, United Kingdom. The tool’s algorithm automatically clustered tweets based on key topics of discussion and sentiment. Tweets contained within the 12 most populated topics with negative sentiment were extracted. The extracted tweets were mapped to one of six pre-determined themes (safety, mistrust, under-representation, complacency, ineffectiveness, and access) informed using the World Health Organization’s 3Cs vaccine hesitancy model. All collated tweets were anonymized.

We identified 91,473 tweets posted between 30 November 2020 and 15 August 2021. A sample of 913 tweets were extracted from the twelve negative topic clusters. Of these, 302 tweets were coded to a vaccine hesitancy theme. ‘Safety’ (29%) and ‘mistrust’ (23%) were the most commonly coded themes; the least commonly coded was ‘under-representation’ (3%). Within the main themes, adverse reactions, inadequate assessment, and rushed development of the vaccines as key findings. Our analysis also revealed widespread sharing of misinformation.

Using an AI-based text analytics tool, we were able to rapidly assess public confidence in COVID-19 vaccination and identify key barriers to uptake from a corpus of geo-located tweets. Our findings support a growing body of evidence and confidence surrounding the use of AI tools to efficiently analyze early sources of soft-intelligence evidence in public health research.

Inappropriate prescribing of antibiotics is a significant driver of antimicrobial resistance (AMR) which is a global health challenge. Technological innovations present an opportunity to reduce demand for antimicrobials through infection prevention, detection, and management. The National Institute for Health and Care Research (NIHR) Innovation Observatory (IO) has developed horizon scanning methods to identify promising innovations (devices/diagnostics/digital) and anticipate technological trends. Together these insights build a comprehensive landscape and presents a significant opportunity for decision-makers and HTAs to consider the clinical, financial, infrastructural, and logistical provisions to improve preparedness for the potential adoption of these future innovations.

The IO developed a detailed dataset of technologies by formulating search strategies for AMR, based on a comprehensive list of terms and input from expert panels. Primary and secondary sources were systematically scanned using a combination of traditional scanning methods, automated and novel artificial intelligence (AI)/machine learning techniques. Sources included clinical trial registries, MedTech news, academic sources, funding agencies, commercial sites, and regulatory authorities.

Our global dataset identified over 3000 innovative preventative, detection, and monitoring technologies mapped across AMR clinical pathways (including sepsis, respiratory tract infections). Development activity largely concentrated in the United States of America and United Kingdom. Emerging trends included the application of novel materials to prevent infections (e.g., catheter coatings) and novel analytical techniques (e.g., biosensors, microfluidics, breath analysis) to support optimal patient treatment. Data analysis revealed a high proportion of technologies were diagnostic innovations addressing unmet needs such as rapid and accurate detection (including drug-resistant infections).

The rapid development and application of technological interventions presents an opportunity to strengthen national AMR strategies worldwide, through the adoption of new innovations. Improvements in exiting technologies, along with technological advancements have the potential to support appropriate prescribing of antimicrobials and thus address the rise in AMR.

The National Institute for Health and Care Research Innovation Observatory (IO) is a horizon scanning centre based at Newcastle University, United Kingdom. The IO provides horizon scanning intelligence on new and innovative medicinal products to the National Institute for Health and Care Excellence (NICE) as technology briefing notifications (TBNs). We present an analysis of how TBNs produced between April 2017 and October 2021 feed into the NICE HTA process and used to inform their Technology Appraisal (TA) programme.

TBNs were mapped to relevant published NICE TA guidance and time from horizon scanning identification to NICE recommendation was studied. For mapping technologies undergoing appraisal, provisional guidance-in-development (GID) identification numbers (IDs) were used. For technologies that had not reached the NICE scoping stage yet, the NICE Topic Selection decision and ID was used.

Six hundred and ninety-three TBNs were submitted to NICE between April 2017 and October 2021; 653 were prioritised for TA. Of those, eleven percent mapped to a published NICE TA guidance; forty-three percent to a GID, twenty-two percent were undergoing consultation, and three percent were not traced. Further twenty-one percent mapped to a suspended or terminated TA. Reasons for this included HTA timeliness, regulatory issues or companies unwilling to submit evidence to NICE. Time from technology identification to TA guidance publication ranged from twenty-two to 115 months. The average time from TBN submission to NICE recommendation was thirty months.

Timely notification is key in achieving TA recommendation aligned with market authorization but not the only influencing factor. After issuing a TBN, the NICE appraisal process might be terminated, suspended or withdrawn due to unforeseen factors. Horizon scanning plays a key role triggering the NICE TA process; understanding factors that influence the successful TA completion would streamline processes and find efficiencies.

There is increasing pressure to rapidly shape policies and inform decision-making where robust evidence is lacking. This work aimed to explore the value of soft-intelligence as a novel source of evidence. We deployed an artificial intelligence based natural language platform to identify and analyze a large collection of UK tweets relating to mental health during the COVID-19 pandemic.

A search strategy comprising a list of terms relating to mental health, COVID-19 and the lockdown was developed to prospectively identify relevant tweets via Twitter's advanced search application programming interface. We used a specialist text analytics platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion for qualitative analysis. All collated tweets were anonymized.

We identified 380,728 tweets from 184,289 unique users in the UK from 30 April to 4 July 2020. The average sentiment score was fifty-two percent, suggesting overall positive sentiment. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. For example, some people described how they were using the lockdown as a positive opportunity to work on their mental health, sharing helpful strategies to support others. However, many people expressed the damaging impact the pandemic (and resulting lockdown) was having on their mental health, including worsening anxiety, stress, depression, and loneliness.

The results suggest that soft-intelligence is potentially a useful source of evidence. The approach taken to identify and analyze this data may offer an efficient means of establishing key insights from the ‘public voice’ relating to critical health issues. However, there are still various limitations to consider concerning the technology and representativeness of the data. Future work to explore this type of evidence further, and how it might formally support decision-making processes, is recommended.

This project is funded by the NIHR [(HSRIC-2016-10009)/Innovation Observatory]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Various strategies to suppress the Coronavirus have been adopted by governments across the world; one such strategy is diagnostic testing. The anxiety of testing on individuals is difficult to quantify. This analysis explores the use of soft intelligence from Twitter (USA, UK & India) in helping better understand this issue.

A total of 650,000 tweets were collected between September and October 2020, using Twitter API using hashtags such as ‘#oxymeter’, ‘#oximeter’, ‘#antibodytest’, ‘#infraredthermometer’, ‘#swabtest’, ‘#rapidtest’, and ‘#antigen’. We applied natural language processing (TextBlob) to assign sentiment and categorize the tweets by emotions and attitude. WordCloud was then used to identify the single topmost 500 words in the whole tweet dataset.

Global analysis and pre-processing of the tweets indicate that 21 percent, seven percent and four percent of tweets originated from the USA, UK, and India respectively. The tweets from #antibody, #rapid, #antigen, and #swabtest were positive sentiments, whereas #oxymeter, #infraredthermometer were mostly neutral. The underlying emotions of the tweets were approximately 2.5 times more positive than negative. The most used words in the tweets included ‘hope’ ‘insurance’, ‘symptoms’, ‘love’, ‘painful’, ‘cough’, ‘fast test’, ‘wife’, and ‘kids’.

The finding suggests that it may be reasonable to infer that people are generally concerned about their personal and social wellbeing, wanting to keep themselves safe and perceive testing to deliver some component of that feeling of safety. There are several limitations to this study such as it was restricted to only three countries, and includes only English language tweets with a limited number of hashtags.

The COVID-19 pandemic led to a significant surge in clinical research activities in the search for effective and safe treatments. Attempting to disseminate early findings from clinical trials in a bid to accelerate patient access to promising treatments, a rise in the use of preprint repositories was observed. In the UK, NIHR Innovation Observatory (NIHRIO) provided primary horizon-scanning intelligence on global trials to a multi-agency initiative on COVID-19 therapeutics. This intelligence included signals from preliminary results to support the selection, prioritisation and access to promising medicines.

A semi-automated text mining tool in Python3 used trial IDs (identifiers) of ongoing and completed studies selected from major clinical trial registries according to pre-determined criteria. Two sources, BioRxiv and MedRxiv are searched using the IDs as search criteria. Weekly, the tool automatically searches, de-duplicates, excludes reviews, and extracts title, authors, publication date, URL and DOI. The output produced is verified by two reviewers that manually screen and exclude studies that do not report results.

A total of 36,771 publications were uploaded to BioRxiv and MedRxiv between March 3 and November 9 2020. Approximately 20–30 COVID-19 preprints per week were pre-selected by the tool. After manual screening and selection, a total of 123 preprints reporting clinical trial preliminary results were included. Additionally, 50 preprints that presented results of other study types on new vaccines and repurposed medicines for COVID-19 were also reported.

Using text mining for identification of clinical trial preliminary results proved an efficient approach to deal with the great volume of information. Semi-automation of searching increased efficiency allowing the reviewers to focus on relevant papers. More consistency in reporting of trial IDs would support automation. A comparison of accuracy of the tool on screening titles/abstract or full papers may help to support further refinement and increase efficiency gains.

This project is funded by the NIHR [(HSRIC-2016-10009)/Innovation Observatory]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

The National Institute for Health Research (NIHR) Innovation Observatory (NIHRIO) is the national Horizon Scanning (HS) organization in England, and the National Institute for Health and Care Excellence (NICE) is its key health technology assessment (HTA) stakeholder. NIHRIO has a remit to notify NICE of innovative technologies with a time horizon of three years prior to regulatory approval in the European Union (EU)/United Kingdom (UK). The notification process produces an initial ‘filtration form’ followed by a ‘technology briefing’ produced 17–20 months prior to licence for those technologies that NICE will consider for appraisal. Since April 2017, NIHRIO has produced ~400 technology briefings. We present an analysis of how this has fed into the NICE HTA process so far.

The analysis mapped NIHRIO's technology briefings (April 2017 – June 2020) with relevant NICE technology appraisal/highly specialized technologies (TA/HST) guidance during the time period. The analysis followed the timeline of technologies from identification during the horizon scanning process to filtration to briefing submission to NICE and entering the TA/HST process to outcome/recommendation given by NICE.

Until June 2020, 496 technology briefings entered the NICE TA/HST scoping process. Forty per cent are in progress, four per cent have had a TA/HST recommendation and three per cent that entered the NICE TA/HST scoping process did not complete it. On average it took less time from briefing submission to NICE recommendation for cancer indications. The time from discovery to NICE recommendation ranged from 115 months to 22 months.

HS for TA/HST is a lengthy process from identification to final recommendation and there is considerable variation in time duration from identification to briefing submission to NICE recommendation. Average time taken from briefing submission to NICE recommendation is shorter for cancer indications and repurposed medicines. A full TA/HST may not be recommended for all technology briefings, rather they may update existing guidance or find different routes of evaluation. Technologies that enter the TA/HST scoping process might be terminated, suspended or discontinued for several reasons which may include lack of company engagement, change in development or regulatory plans by the company. Timely notification is key in achieving TA/HST recommendation at the time of market authorization but not the only influencing factor.

Advanced Therapy Medicinal Products (ATMPs) are innovative biologics (gene, cells and tissue-based products) with the potential to treat diseases with significant unmet clinical need. ATMPs pose distinct regulatory, health technology assessment (HTA) and patient access challenges, hence early identification and prioritization of ATMPs is now recognized as a key concern in England. The National Institute for Health Research Innovation Observatory (NIHRIO) uses a robust methodology to identify and monitor health technologies, including ATMPs that meet the remit of key HTA stakeholders in England. This analysis provides a global overview of the current ATMPs pipeline to administer useful insights for policymakers, funders and innovators.

NIHRIO's database tracks pharmaceuticals from phase I/II onwards, but this analysis focuses on late-stage development. The database (N > 12,000 records) was filtered to identify potential ATMPs using a predefined criteria based on the European Medicine's Agency's classification. Each record is categorized by stage: ‘Active’, (with an estimated three years to European licence); ‘Monitoring’ (in development with no licence date); and ‘Finished’, (output produced/discontinued and no longer tracked). Subsequently, records in ‘Active’ and ‘Monitoring’ were examined further.

Analysis identified 636 ATMPs: five percent ‘Active’, 40 percent ‘Monitoring’ and 55 percent ‘Finished’. ATMPs in the Active/Monitoring stages included: gene therapies (52%), somatic cells (43%) and tissue-engineered products (5%). Of these, 40 percent were oncological with the majority targeting hematological cancers (lymphomas). Prevalent non-oncology areas included musculoskeletal (10%) and ophthalmology (8%). Over one-third of trials were phase IIs, with almost half of all trials were based in the US.

The overarching findings here indicate increasing development of the ATMP pipeline towards indications with significant unmet clinical need. In oncology, the high prevalence of hematological ATMPs is largely due to recent chimeric antigen receptor T cells (CAR-T) innovation. In non-oncology areas, ATMP development is increasing due to advances in regenerative medicine. With a significant number of ATMPs projected to be licenced within three years, and many more in active late-stage trials, HTA bodies and health systems are challenged to prepare for the entry of these innovative therapies.