Contact Us

Home

About us

Services

AI Excellence

case studies

publications

HEOR Certificationheor Certification

AI for Medical Writing

Contact

  • Health Technology Assessment of Medical Devices in Asia Pacific Region

    Health Technology Assessment of Medical Devices in Asia Pacific Region

    Health Technology

    The importance of Health technology assessment (HTA) in influencing the assessment, approval, and adoption of medical devices in healthcare systems is increasingly being realised, particularly in the Asia Pacific (APAC) region.(1) Unlike pharmaceuticals where HTA frameworks are comparatively mature and globally consistent, HTA for medical devices poses unique challenges with their rapid innovation cycles, shorter lifespans, iterative design developments, and requirement of user training and system readiness. These challenges make their evaluation complicated, yet increasingly important, as policymakers must assess technological progress with fair access and sustainable healthcare expenditure.(1-3) 

    The APAC is a region known for its huge diversity, ranging from high-income countries with well-established health systems to developing countries with limited resources and vast unmet clinical needs.(4) This diversity necessitates distinct HTA methodologies. For instance, countries like Australia, South Korea, Japan have HTAs well-integrated into reimbursement and procurement decisions,(2, 5-9) while others like India, Thailand, and the Philippines are still developing institutional guidance and capability. Middle-income countries usually only barely focus on costs and limit coverage of new innovations, while higher-income countries use HTA to improve both cost and utilization, largely encouraging broader coverage if technologies meet clinical and economic criteria.(10) As a result, HTA practices in the APAC region are variable, ranging from highly organized systems to emerging models just beginning to incorporate evidence-based assessments into policy decisions.(3, 4)  

    Medical devices are dynamic and context-sensitive, which makes real-world evidence (RWE), as well as data from local health registries and pragmatic trials, particularly important. In addition, differences in infrastructure, cultural backgrounds, and epidemiological patterns may hinder the applicability of evidence across healthcare settings.(2-4) 

    In the APAC region, South Korea requires medical technologies seeking reimbursement to undergo HTA evaluation by the Health Insurance Review and Assessment Service (HIRA), while also using approaches like conditional approval, rapid review, and coverage with evidence development to balance timely access with rigorous evaluation.(5) In Australia, HTA evaluations for medical devices are performed by the Medical Services Advisory Committee (MSAC), requiring systematic reviews and pharmacoeconomic evidence but also often apply alternative strategies, like linked evidence approaches, when randomized trials are unfeasible, while also taking into account equity and ethical concerns along with cost-effectiveness.(6, 7) 

    In contrast, Japan has a unique model with medical device reimbursement heavily reliant on comparisons with available functional categories or cost-calculation approaches.(8) Japan’s HTA process (since 2019) necessitates cost-effectiveness analysis for expensive devices, with price cuts upon failure to demonstrate value. (8, 9) Other APAC countries, including China, India, Thailand, and Indonesia, are in earlier stages of HTA for medical devices, primarily emphasizing cost containment and limited coverage, but are gradually evolving towards guidance adapted to local healthcare needs and increasing regional collaboration.(2-4) 

    By integrating context-specific considerations, HTA frameworks in the APAC region are expected to go beyond narrow cost-control strategies to achieve tactical investments in technologies that truly cater to the local preferences, sustainability, and equitable access.(3, 4) The future of medical device HTA in APAC region warrants collective and adaptive strategies, including digital therapeutics and AI-powered diagnostics, with many countries beginning to implement these advances in their evaluations. Initiatives like HTAsiaLink are also instrumental in promoting knowledge-sharing and avoiding effort duplication, while global partnerships and capacity-building programs continue to support local expertise.(11) With countries investing in data networks, standardizing approaches, and focusing on fair access, HTA will continue to guide decisions on reimbursement and procurement, thus shaping innovation that aligns with the health preferences of diverse populations across the region. 

    Become A Certified HEOR Professional – Enrol yourself here!

    References 

    1. Liu G, Wu EQ, Ahn J, Kamae I, Xie J, Yang H. The Development of Health Technology Assessment in Asia: Current Status and Future Trends. Value Health Reg Issues. 2020; 21:39-44. 
    2. Shiroiwa T, Fukuda T, Ikeda S, Takura T, Moriwaki K. Development of an Official Guideline for the Economic Evaluation of Drugs/Medical Devices in Japan. Value Health. 2017; 20(3):372-378. 
    3. Bhattacharyya D. HTA of Medical Devices in Asia Pacific. ISPOR Asia Pacific 2018. [Accessed online on 5th September 2025]. Available online at: https://www.ispor.org/docs/default-source/conference-ap-2018/hta_of_medical_devices_in_asia_pacific_devarshi.pdf?sfvrsn=b71c1b82_0 
    4. Kao C, Lakhanpal S. Health Technology Assessment (HTA) for Medical Devices in Asia Pacific – Webinar Summary. APACMed – Avalere Health. [Accessed online on 5th September 2025]. Available online at:  https://apacmed.org/wp-content/uploads/2024/04/HTA-for-medical-devices-in-Asia-Pacific_FINAL.pdf 
    5. Oh J, Kim MJ, Hur S, Oh J, Kim DS. Institutionalizing Health Technology Assessment and Priority Setting in South Korea’s Universal Health Coverage Journey. Health Systems & Reform. 2023; 9(3). 
    6. Hill H, Mittal R, Merlin T. Evidence-based funding of new imaging applications and technologies by Medicare in Australia: How it happens and how it can be improved. J Med Imaging Radiat Oncol. 2022; 66(2):215-224. 
    7. Centre for Health Economics Research & Evaluation (CHERE) – University of Technology, Sydney. Health Technology Assessment Policy and Methods Review: HTA Methods: Economic Evaluation. [Accessed online on 8th September 2025]. Available online at:  https://www.health.gov.au/sites/default/files/2024-07/hta-policy-and-methods-review-hta-methods-economic-evaluation.pdf 
    8. Tamura M, Nakano S, Sugahara T. Reimbursement pricing for new medical devices in Japan: Is the evaluation of innovation appropriate? Int J Health Plann Manage. 2019; 34(2):583-593. 
    9. Hasegawa M, Komoto S, Shiroiwa T, Fukuda T. Formal Implementation of Cost-Effectiveness Evaluations in Japan: A Unique Health Technology Assessment System. Value Health. 2020; 23(1):43-51. 
    10. Alkhaldi M, Al Basuoni A, Matos M, Tanner M, Ahmed S. Health Technology Assessment in High, Middle, and Low-income Countries: New Systematic and Interdisciplinary Approach For Sound Informed-policy Making: Research Protocole. Risk Manag Healthc Policy. 2021; 14:2757-2770. 
    11. HTAsiaLink. [Accessed online on 5th September 2025]. Available online at: https://htasialink.com/about-us 

     

    MarksMan Healthcare

  • Selecting Appropriate Review Approach for HTA Submissions

    Selecting Appropriate Review Approach for HTA Submissions

    Formulating Fit-for-Purpose Evidence Packages to Demonstrate the Value of Interventions for Rare Diseases

    Health Technology Assessments (HTAs) heavily rely on vigorous evidence synthesis. As a result, the selection of review methodology for evidence generation is an essential early step in the HTA process. This selection usually depends on the research question, nature and extent of existing evidence, and assessment timelines.(1) Three distinct approaches for reviewing existing evidence include de novo systematic review, an update of an existing review, and an overview of systematic reviews.(1, 2)

    A de novo systematic review is performed when no reliable review is available on the topic, or when the available ones are outdated, incomplete, or methodologically weak. This method enables an exhaustive and unbiased evaluation of the evidence from scratch, allowing for the customization of inclusion criteria, quality appraisal, and data synthesis as per the specific HTA’s objectives. This is also the most resource-consuming option, demanding substantial time and effort to select and analyse the relevant literature.(2-4)

    An update of an existing systematic review may be performed if a high-quality review is already present in public domain; here the objective would be to update the evidence using publications reported after the publication of the review. In rapidly evolving therapeutic areas with frequently emerging trials and interventions, updating an existing review provides efficiency while preserving the integrity of the evidence. This approach facilitates HTA bodies to expand on prior work, integrate new data, and improve inferences without repeating the entire review process.(2, 4, 5) In this context, the use of living systematic reviews (living SLRs), which are constantly updated with new and upcoming evidence, can also be explored. By integrating real-time updates, living SLRs minimize the delay between research publication and evidence generation, making them especially important in dynamic therapeutic areas where well-timed decisions are crucial.(6, 7)

    An overview of systematic reviews, often referred to as an umbrella review, is apt when multiple high-quality reviews have already been performed on closely related interventions or outcomes. This methodology incorporates findings across reviews, focusing on not just consistencies but also differences and evidence gaps. It is especially important when HTAs are required to assess wide-ranging policy questions or making decisions encompassing multiple treatment options, therapeutic areas, or patient subgroups. By incorporating insights across systematic reviews, their overviews offer an enhanced understanding of the evidence setting that guides strategic decision-making.(2, 3, 8)

    In some cases, selection of other types of reviews may also be applicable. For instance, scoping reviews can be especially beneficial during the exploratory phase, where the aim is to determine the scope of available evidence, elucidate concepts, or characterize knowledge gaps before providing final answers. Such scoping reviews help refine research questions and assess if a full systematic review is needed.(1) On the other hand, rapid reviews are used when HTA-relevant decisions must be made under strict timelines (e.g., during public health emergencies or early advice procedures), in which conventional systematic review steps are streamlined while preserving transparency. In contrast, targeted literature reviews are applied in narrower contexts, such as epidemiology assessments or comparator identification, where a structured but not fully systematic evidence synthesis is sufficient. However, both rapid and targeted reviews are generally less favoured in HTA because their methodological shortcuts increase the risk of bias and limit reproducibility compared with full systematic reviews.(9, 10) Agencies like Canadian Agency for Drugs and Technologies in Health (CADTH) are leading the development of practical guidance on when each review approach is most suitable, facilitating global HTA agencies to approve fit-for-purpose methods.(2)

    Finally, the selection of any of these review types should be based on the scope of the HTA, available timelines, and the state of the evidence landscape. A rationalized decision not only facilitates methodological strength but also improves the integrity and relevance of the HTA’s recommendations. With the expanding evidence network, careful selection of the review approaches will continue to be essential for accomplishing timely, efficient, and impactful HTAs.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Nemzoff C, Shah HA,  Heupink LE, et al. Adaptive Health Technology Assessment: A Scoping Review of Methods. Value Health. 2023; 26(10):P1549-1557.
    2. Kim JSM, Pollock M, Kaunelis D, Weeks L. Guidance on review type selection for health technology assessments: key factors and considerations for deciding when to conduct a de novo systematic review, an update of a systematic review, or an overview of systematic reviews. Syst Rev. 2022; 11(1):206.
    3. Pollock M, Fernandes RM, Becker LA, et al. What guidance is available for researchers conducting overviews of reviews of healthcare interventions? A scoping review and qualitative metasummary. Syst Rev. 2016;5(1):190.
    4. Cumpston M, Chandler J, et al. Chapter IV: Updating a review. In: Higgins J, Thomas J, Chandler J, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.2 (updated February 2021) London: Cochrane; 2021.
    5. Garner P, Hopewell S, Chandler J, et al. When and how to update systematic reviews: consensus and checklist. BMJ. 2016; 354:i3507.
    6. Simmonds M, Elliott JH, Synnot A, Turner T. Living Systematic Reviews. Methods Mol Biol. 2022; 2345:121-134.
    7. Sauca M, Tarchand R, Kallmes K. Living SLRs for HTA. ISPOR Europe 2023. [Accessed online on 1st Sept 2025]. Available at: https://www.ispor.org/docs/default-source/euro2023/isporeurope23saucahta361poster-129656-pdf.pdf?sfvrsn=ca7a753b_0
    8. Pollock M, Fernandes RM, Becker LA, Pieper D, Hartling L. Chapter V: Overviews of reviews. In: Higgins JPT, Thomas J, Chandler J, et al., Cochrane Handbook for Systematic Reviews of Interventions. Version 6.2 (updated February 2021). London: Cochrane; 2021.
    9. Kaltenthaler E, Cooper K, Pandor A, et al. The use of rapid review methods in health technology assessments: 3 case studies. BMC Med Res Methodol. 2016; 16(1):108.
    10. Watt A, Cameron A, Sturm L, et al. Rapid reviews versus full systematic reviews: an inventory of current methods and practice in health technology assessment. Int J Technol Assess Health Care. 2008; 24(2):133-9.

    MarksMan Healthcare

    ,
    , , , , ,

  • The Win Ratio Method to Assess the Value of Interventions in Rare Diseases

    The Win Ratio Method to Assess the Value of Interventions in Rare Diseases

    Assessing the value of interventions in rare diseases poses unique challenges due to the small patient populations and the heterogeneity of these conditions. Traditional statistical methods often fall short of providing robust and meaningful analyses. The Win Ratio method, an innovative statistical approach, has emerged as a valuable tool to address these challenges and provide a more nuanced evaluation of treatment effects.[1]

    In 2012, Pocock et al. introduced the win ratio approach to enhance the analysis of composite endpoints in randomized controlled clinical trials. This method involves evaluating whether a participant on an experimental treatment performed better (a “win”), worse (a “loss”), or about the same (a “tie”) as a participant on the control treatment across a series of endpoints, prioritized by their clinical importance. Traditional reporting of composite endpoints often emphasizes the first event for each patient, which may be of lesser clinical importance. The win ratio approach addresses this limitation by offering a more meaningful way to report composite endpoints.[2,3]

    The Win Ratio method involves pairwise comparisons between patients in treatment and control groups, forming matched pairs based on risk profiles. Each pair is assessed: the patient experiencing a death first is labeled a ‘loser,’ and if this event is unknown, the next priority event, hospitalization, is considered; if neither event occurs first, the pair is tied. The win ratio is calculated as the total number of winners divided by the total number of losers, allowing simultaneous consideration of multiple outcomes prioritized by clinical importance, unlike traditional methods focusing on a single primary endpoint.[1-3]

    Two approaches have been described for calculating the win ratio. In the Matched Pair Approach, matched pairs are selected from treatment and control groups using risk scores and outcomes are compared based on a hierarchy of events. In the Unmatched Pair Approach, every patient in the treatment group is compared with every patient in the control group. The win ratio method offers a robust approach to assessing multiple clinical outcomes, providing a clearer picture of treatment efficacy. The process of scoring wins, ties, and losses involves comparing outcomes according to the hierarchy until a win, loss, or tie is determined. A Win Ratio greater than 1 indicates a favorable outcome for the treatment, while a ratio less than 1 suggests a favorable outcome for the control.[2,4-6]

    Studies have indicated that the Win ratio method might be particularly useful in rare disease research. Since rare diseases are a highly heterogeneous group of disorders marked by significant phenotypic and genotypic diversity within individual conditions, the limited number of affected individuals presents unique challenges in understanding these diseases and developing treatments. Notable challenges in rare disease research include patient identification, recruitment, high costs, small sample sizes, disease heterogeneity, and the need for specialized trial designs and multiple endpoints.[1] The Win Ratio method is advantageous in rare disease research due to its flexibility in endpoint selection and the ability to incorporate multiple clinically relevant endpoints. This method addresses concerns about composite primary endpoints in randomized trials, where less significant events like hospitalizations may dominate. By focusing on more clinically important components and allowing repeated analyses with different randomly removed patients to calculate a median win ratio, it ensures comprehensive data use and sequential outcome assessment. It is particularly effective for small sample sizes, maximizing data use, enhancing statistical power, and prioritizing important endpoints.[2,3,7-9]

    The Win Ratio method has demonstrated its utility in various rare disease studies by effectively integrating multiple clinically relevant endpoints. In a clinical trial for amyotrophic lateral sclerosis (ALS), for instance, the method incorporated survival, functional decline, and respiratory function into the analysis. This comprehensive approach revealed a significant treatment effect that traditional methods missed, highlighting the Win Ratio’s value in rare disease research. Another example is its application in the COMET phase 3 trial for late-onset Pompe disease, showcasing its ability to analyze multiple endpoints in the context of orphan drug development. These examples underscore the method’s effectiveness in capturing the full scope of treatment impacts in rare diseases.[2,3]

    While the Win Ratio method offers significant advantages, it also presents some challenges. Establishing the hierarchy of endpoints requires careful consideration and consensus among clinical experts. Critics argue that the Win Ratio can overestimate treatment effects by ignoring tied pairs and equally weighting each hierarchical component, potentially leading to fallacies in clinical interpretation. Another major issue is that ties (neither a win nor a loss) are often disregarded, which can skew results. Clinicians should not rely solely on the Win Ratio statistic but must understand the overall treatment effect by accounting for ties and dropouts to ascertain meaningful measures such as the number needed to treat or harm. Assessing aggregate clinical benefits is necessary to translate trial data into a comprehensive understanding of the magnitude, temporal relationships, durability, and overall value of the treatment, including cost-effectiveness. Additionally, the method’s complexity may require specialized statistical expertise, posing a barrier for some research teams.[10-12]

    The Win Ratio method represents a significant advancement in the assessment of interventions for rare diseases. Its ability to handle multiple endpoints, small sample sizes, and clinically relevant outcomes makes it a powerful tool for evaluating treatment effects. As the field of rare disease research continues to evolve, the Win Ratio method is likely to play an increasingly important role in guiding clinical and regulatory decisions, ultimately improving outcomes for patients with rare conditions.

    Become A Certified HEOR Professional – Enrol yourself here!

    References:

    1. Liu J, Barrett JS, Leonardi ET, Lee L, et al. Natural history and real‐world data in rare diseases: applications, limitations, and future perspectives. The Journal of Clinical Pharmacology. 2022 Dec;62:S38-55.
    2. Pocock SJ, Ariti CA, Collier TJ, Wang D. The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. European heart journal. 2012 Jan 1;33(2):176-82.
    3. Boentert M, Berger KI, Díaz-Manera J, et al. Applying the win ratio method in clinical trials of orphan drugs: an analysis of data from the COMET trial of avalglucosidase alfa in patients with late-onset Pompe disease. Orphanet Journal of Rare Diseases. 2024 Jan 12;19(1):14.2024;19(1):14.
    4. Hongyue WA, Jing PE, Zheng JZ, et al. Win ratio–An intuitive and easy-to-interpret composite outcome in medical studies. Shanghai Archives of Psychiatry. 2017 Feb 2;29(1):55.
    5. Finkelstein DM, Schoenfeld DA. Combining mortality and longitudinal measures in clinical trials. Statistics in medicine. 1999 Jun 15;18(11):1341-54.
    6. Luo X, Tian H, Mohanty S, Tsai WY. An alternative approach to confidence interval estimation for the win ratio statistic. Biometrics. 2015 Mar;71(1):139-45.
    7. Freemantle N, Calvert M, Wood J, Eastaugh J, Griffin C. Composite outcomes in randomized trials: greater precision but with greater uncertainty?. Jama. 2003 May 21;289(19):2554-9.
    8. Ishak KJ, Caro JJ, Hamed A, et al. SA26 Win Ratio Analyses of Multiple Endpoints in Rare Disease Trials: A Case-Study Based on a Trial of Avaglucosidase Alfa in Late-Onset Pompe Disease (LOPD). Value in Health. 2022 Dec 1;25(12):S487-8.
    9. Montori VM, Permanyer-Miralda G, Ferreira-González I, et al. Validity of composite end points in clinical trials. Bmj. 2005 Mar 10;330(7491):594-6.
    10. Verbeeck J, Dirani M, Bauer JW, et al. Composite endpoints, including patient reported outcomes, in rare diseases. Orphanet Journal of Rare Diseases. 2023 Sep 1;18(1):262.
    11. Dong G, Hoaglin DC, Qiu J, et al. The win ratio: on interpretation and handling of ties. Statistics in Biopharmaceutical Research. 2020 Jan 2.
    12. Ajufo E, Nayak A, Mehra MR. Fallacies of using the win ratio in cardiovascular trials: challenges and solutions. Basic to Translational Science. 2023 Jun 1;8(6):720-7.

    MarksMan Healthcare

  • Deduplication of Records in Systematic Literature Reviews: Why and How?

    Deduplication of Records in Systematic Literature Reviews: Why and How?
    Deduplication of Records in Systematic Literature Reviews: Why and How?

    Systematic literature reviews (SLRs) play a pivotal role in evidence-based decision-making by synthesizing existing research to guide scientific practice. The meticulous process of exhaustive literature searches in SLRs often employs literature searching in multiple databases and other sources of information. Naturally, at the end of the literature searching activity, a sizeable number of records in the pool of initial potential hits might be duplicated, by being captured from different information sources. The identification and removal of such duplicate records is critical to ensure the credibility and transparency of an SLR. [1]

    The inclusion of duplicate records in SLRs can have far-reaching consequences. One of the primary impacts is the inflation of the apparent number of studies. This can lead to a distorted view of the existing evidence, potentially affecting the overall conclusions drawn from the review. The reliability of findings is at stake when duplicate data is included, influencing the analysis towards certain studies or authors and compromising the impartiality of the review. The importance of thorough deduplication becomes evident not only for the sake of accuracy but also for maintaining efficiency and reducing bias in the systematic review process.[1] Thus, proper deduplication ensures that the conclusions drawn from the SLR are based on a fair representation of the available evidence, free from the distorting effects of redundant information.[2]

    Deduplication strategies in systematic reviews encompass a spectrum of approaches, ranging from manual screening to the utilization of advanced automated tools. Reference management software, such as EndNote, Mendeley, or Zotero, offers built-in deduplication features based on metadata matching. However, these tools may overlook duplicates with minor variations or different publication formats. Automated deduplication tools like Rayyan, Covidence, or Systematic Review Assistant employ sophisticated algorithms that consider title and abstract similarity, full-text comparisons, and citation matching. While these tools enhance accuracy, manual screening remains indispensable, particularly for cases with significant variations in title or authorship.[3]

    Effective deduplication involves the development of a clear and documented protocol outlining specific criteria and tools. Multiple search runs are often necessary to consolidate results, and regular calibration of automated tools against manual identification is crucial. Transparent documentation of the deduplication process in the review report is instrumental in enabling other researchers to replicate the review steps and contribute to the cumulative knowledge in the field.[1]

    The evolution of deduplication methods reflects the continuous quest for more effective tools. From manual cataloguing in the early days of bibliographic record-keeping to sophisticated automated techniques, the landscape of deduplication has undergone significant changes. Reference management software introduced automated deduplication features, and the advent of Digital Object Identifiers (DOIs) in the late 1990s facilitated more accurate deduplication. Researchers have explored various methods and tools, each with its strengths and limitations. Recent advancements include the introduction of dedicated tools such as Deduklick, an artificial intelligence-based algorithm that combines natural language processing with expert-defined rules. The Bramer method, introduced in 2016, focuses on adapting page number formats to facilitate deduplication in EndNote. The Systematic Review Assistant-De-duplication Module (SRA-DM), developed in 2015, demonstrated superior sensitivity and specificity compared to EndNote’s default deduplication process. [4]

    However, deduplication is not without its challenges. Non-standard citations, variations in database indexing, cross-language studies, and the existence of multiple versions of the same studies pose hurdles for both automated and manual deduplication. The variability in data, differences in citation formats, and the risk of exclusion due to overly aggressive deduplication demand careful consideration.[1]

    In conclusion, deduplication is a critical step in maintaining the accuracy, reliability, and transparency of SLRs. The evolving landscape of tools and methodologies underscores the continuous commitment to refining and advancing deduplication practices. As technology continues to progress, collaboration across disciplines and ongoing research will shape the future of deduplication, paving the way for more effective and efficient processes in evidence synthesis. Researchers and reviewers must remain vigilant, adopting best practices and considering the challenges inherent in the deduplication process to uphold the highest standards of scientific rigor in SLRs.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Hammer B, Virgili E, Bilotta F. Evidence-based literature review: De-duplication a cornerstone for quality. World Journal of Methodology. 2023 Dec 12;13(5):390.
    2. Kwon Y, Lemieux M, McTavish J, Wathen N. Identifying and removing duplicate records from systematic review searches. J Med Libr Assoc. 2015 Oct;103(4):184-8. doi: 10.3163/1536-5050.103.4.004. PMID: 26512216; PMCID: PMC4613377.
    3. Puljak L, Lund H. Definition, harms, and prevention of redundant systematic reviews. Systematic Reviews. 2023 Apr 4;12(1):63.
    4. Rathbone J, Carter M, Hoffmann T, Glasziou P. Better duplicate detection for systematic reviewers: evaluation of Systematic Review Assistant-Deduplication Module. Systematic reviews. 2015 Dec;4:1-6.

    MarksMan Healthcare

  • Generating Clinical Evidence From Electronic Health Records and Patient Registries

    Generating Clinical Evidence From Electronic Health Records and Patient Registries

    Both electronic health records (EHRs) and patient registries store and use patient-related clinical information. However, they are conceptualized for different purposes. Both are a significant source of real-world evidence (RWE) as they gather a considerable amount of clinical information collected in the real-world setting.

    An EHR is an electronic record of health data generated during routine patient care delivered by healthcare providers.(1) EHRs are widely used to obtain information on several medical parameters from patients and maintain clinical workflows.(2) They usually comprise data on patients’ demographic, vitals, administrative, claims (medical and pharmacy), and clinical parameters. They also include other patient-related information, such as data from health-related quality-of-life instruments, home-monitoring devices, and caregiver assessments.(3)

    EHRs usually represent individual care structures, such as primary, emergency, and intensive care units, and are visit-oriented and transferable. They also offer data from integrated systems in single or linked hospitals.(4) As the use of EHRs becomes extensive in clinical research, it is only ideal that they are designed to enhance diagnosis and clinical care to improve their relevance further. The design of EHRs can also update with time as the technology advances or depending on external factors, such as changes in data type as per coding or reimbursement patterns.(1, 3)

    A patient registry is “an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure and that serves one or more predetermined scientific, clinical, or policy purposes.”(1) Patient registries are crucial in research as an ultimate platform for focused information about patients with specific health conditions. They often also help answer questions otherwise not answered by randomized clinical trials (RCTs), owing to practicality or ethicality. Registry data also help reduce the time and cost of prospective data collection. RWD generated in registries enables hypotheses generation in research, thus helping descriptive studies and research in health services.(5) Registries are typically patient-oriented and goal-driven. They are designed to collate information on specific exposures and health outcomes. Patient registries can be population-based or hospital-based.

    Often data captured from EHRs are used to construct patient registries. Specifically, EHRs can facilitate certain functions for patient registries, such as collection, cleaning, and storage of data. Likewise, a registry can enhance the value of the information gathered in the EHRs, for instance, comparative effectiveness, safety, and value, population management, and quality reporting, among others.(6)

    Data from EHRs, either as stand-alone or as complementary information to the primary research or data from administrative databases, have been used to support observational studies. For instance, the Euro Heart Survey (7), the Eurobservational Research Program (EORP) which followed the survey,(8) and the AHA Get With the Guidelines (AHA GWTG) (9) show clinical information from EHRs on several cardiovascular diseases.(3) Moreover, the EU-ADR project connects eight databases from four European countries (United Kingdom, Italy, The Netherlands, and Denmark) to facilitate the analysis of specific target adverse drug reactions (ADRs).(10) The USFDA uses data from EHRs from various sources, including sentinel systems,(11) claims databases (Medicare and Medicaid Services), and Veterans Affairs, among others, to support safety investigations for products after marketing approvals.(12)

    Many clinical registries across the globe comprise patient data on acute and chronic stages of different diseases, such as cancer, cystic fibrosis, and multiple sclerosis, to name a few. For instance, countries like the US, Canada, Australia, Germany, Sweden, and Argentina, have registries to monitor and store patient data on acute stroke. The Cystic Fibrosis Foundation Patient Registry is a clinical quality registry (CQR), developed from an epidemiological and clinical research model.(13) The American Heart Association (AHA) recommends 5 key concepts in establishing patient registries: ensuring high quality data, linking registries with relevant supplemental data, integrating registries with EHRs, safeguarding privacy, and funding considerations.(14) CQRs have also been reported to contain extensive clinical information that can complement data from government-monitored registries. These data are vital for assessing the quality of care and research.(5, 15)

    Clinical data from both EHRs and registries can generate meaningful evidence to enhance trial efficiency and optimize novel research approaches. These RWD sources can help comparative effectiveness research while facilitating new trial designs to address unmet clinical needs. Their use seems hopeful. However, the technological advancements in these sources need to be looked at with applicable care measures to ensure data privacy and confidentiality.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Ehrenstein V, Kharrazi H, Lehmann H, et al. Obtaining Data From Electronic Health Records. In: Gliklich RE, Leavy MB, Dreyer NA, editors. Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User’s Guide, 3rd Edition, Addendum 2 [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2019 Oct. Chapter 4. Available from: https://www.ncbi.nlm.nih.gov/books/NBK551878/
    2. Gliklich R, Dreyer N, Leavy M, eds. Registries for Evaluating Patient Outcomes: A User’s Guide. Third edition. Two volumes. (Prepared by the Outcome DEcIDE Center [Outcome Sciences, Inc., a Quintiles company] under Contract No. 290 2005 00351 TO7.) AHRQ Publication No. 13(14)-EHC111. Rockville, MD: Agency for Healthcare Research and Quality. April 2014. Available from: http://www​.effectivehealthcare.ahrq.gov
    3. Cowie MR, Blomster JI, Curtis LH, et al. Electronic health records to facilitate clinical research. Clin Res Cardiol. 2017; 106(1):1-9.
    4. Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008; 77:291–304.
    5. Hoque DME, Kumari V, Hoque M, et al. Impact of clinical registries on quality of patient care and clinical outcomes: A systematic review. PLOS ONE. 2017; 12(9): e0183667.
    6. Gliklich RE, Dreyer NA, Leavy MB. Interfacing Registries With Electronic Health Records. Registries for Evaluating Patient Outcomes: A User’s Guide. 2. Third ed. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ); 2014. p. 3–22.
    7. Scholte op Reimer W, Gitt A, et al. Cardiovascular diseases in Europe. Euro Heart Survey−2006. European Society of Cardiology, 2006.
    8. Ferrari R. EURObservational research programme. Eur Heart J. 2010; 31:1023–1031.
    9. Smaha LA. The American Heart Association Get with the Guidelines program. Am Heart J. 2004; 148:S46–S48.
    10. Trifiro G, Fourrier-Reglat A, Sturkenboom MC, et al. The EU-ADR project: preliminary results and perspective. Stud Health Technol Inform. 2009; 148:43–49.
    11. Ball R, Robb M, Anderson SA, Dal Pan G. The FDA’s sentinel initiative-A comprehensive approach to medical product surveillance. Clin Pharmacol Ther. 2016; 99:265–268.
    12. Staffa JA, Dal Pan GJ. Regulatory innovation in postmarketing risk assessment and management. Clin Pharmacol Ther. 2012; 91:555–557.
    13. Schechter MS, Fink AK, Homa K, Goss CH. The Cystic Fibrosis Foundation Patient Registry as a tool for use in quality improvement. BMJ quality & safety. 2014; 23(Suppl 1):i9–i14.
    14. Bufalino VJ, Masoudi FA, Stranne SK, et al. The American Heart Association’s recommendations for expanding the applications of existing and future clinical registries a policy statement from the American Heart Association. Circulation. 2011; 123(19):2167–79.
    15. Emilsson L, Lindahl B, Koster M, et al. Review of 103 Swedish Healthcare Quality Registries. Journal of Internal Medicine. 2015; 277(1):94–136.

    MarksMan Healthcare

    , ,

  • How Patient Records Abstraction Can Help in Healthcare Decision Making?

    How Patient Records Abstraction Can Help in Healthcare Decision Making?

    Patient Records Abstraction (PRA) is a process done manually by searching through a medical record to identify data required for a particular or secondary use. It consists of direct matching of information found in the record to the data required, but also includes operations on the data such as categorizing, coding, transforming, interpreting, summarizing, and calculating. The abstraction, in the end, summarizes information about a patient for a specific secondary data use. (1) PRA typically involves reviewing patient files and abstracting (i.e., extracting) key data, which are then entered into electronic files. (2) Depending on the measure or purpose, there can be different sources for data collection such as paper medical records, electronic medical records (EMR), patient surveys, administrative databases, etc.

    PRA helps in reviewing large or small data sets and documents for information which can be helpful in the future for decision making. (3) It often involves collecting organizationally-defined, clinically relevant data elements, which don’t electronically convert, from the legacy system into the new target system. This process, therefore, makes detailed patient data instantly available in the electronic chart in a faster, accurate and cost-effective manner; (4) facilitating access to care without referring to a paper chart or an EMR. (5)

    In order to make informed decisions with the help of PRA, a tool referred to as ‘abstraction hierarchy’ (AH) is often implemented to facilitate cognitive work analysis (CWA). (6) These hierarchies can be used to develop depictions of patient care in line with biomedical knowledge, making medical problem solving easier, and act as a frame of reference. (7)

    Studies exploring different aspects of AH also suggest that, it can be useful in implementing shared decision making (SDM) in order to improve patient care through their active engagement. Implementing SDM would be an advantageous approach to care, as patient involvement in decision making can result in improved health outcomes, thus providing an enhanced ethical framework for clinicians to deliver appropriate care and improved efficiency of the health system.7

    Researchers have found a way to mine huge amounts of patient data with the widespread use of EMR for identifying the best predictors of health outcomes. Also, every EMR system possibly has only a subset of the information necessary for a particular clinical trial.  This is where PRA can come into play to provide the necessary, overall data, thus substantially saving time and energy. (8) Consistent improvement in healthcare data quality plays a vital role in planning, development, and maintenance of healthcare services. This improvement can affect clinical and administrative decision making in many ways, thereby increasing patient safety, and facilitating the efficacy of clinical care pathways. (9)

    In addition to these advancements in healthcare technologies, the concept of ‘Big Data’ is emerging, which seems to have been first derived from an IT strategic consulting group’s approach to manage volume, velocity, and variety of data. (10) Researchers believe that the EMRs that contain huge volumes of patient data in variety of domains could also be considered as ‘Big Data’.  This is owing to the reports stating the United States alone will soon witness one billion patient visits documented per year in EMR systems. (11) Besides, the amount of additional data available about medical conditions, underlying genetics, medications, and treatment approaches is high. (12)

    Furthermore, the use of ‘real-world data’ (RWD) that contributes to the ‘real-world evidence’ (RWE) is also on the rise. RWD and associated RWE may constitute valid scientific evidence depending on the characteristics of the data. For making better choices about health and health care requires the best possible evidence. Sadly, many decisions made today lack the high-quality evidence derived from randomized, controlled trials or well-designed observational studies. Therefore, rich, diverse sources of digital data such as, EMRs, claims data, consumer data, chart reviews, which can cumulatively be referred to as ‘Big Data’- are becoming widely available for research, thus facilitating data extraction with the help of robust abstraction tools. To add to this, concept of chart abstraction methodologies that will integrate physician insights is also emerging. This will ensure better understanding of- i) various ways to address common obstacles and limitations to RWD collection, and ii) the importance community physician relationships for study implementation and success. The research and health care communities, consequently, have the opportunity to support improved healthcare decision making. (13)

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. Nahm M. Data accuracy in medical record abstraction. The University of Texas School of Health Information Sciences at Houston.
    2. Half R. What skills do you need to be an electronic medical records abstractor/auditor? July, 2016.
    3. Rasmussen D. How data abstraction is going to change healthcare forever. September, 2016.
    4. Improving patient records: Conclusions and Recommendations. The computer-based patient record: An essential technology for health care. 1997.
    5. Nahm M. Data accuracy in medical record abstraction. The University of Texas School of Health Information Sciences at Houston.
    6. St-Maurice JD, Burns CM. Modeling Patient Treatment With Medical Records: An Abstraction Hierarchy to Understand User Competencies and Needs. Eysenbach G, ed. JMIR Human Factors 2017; 4(3):e16.
    7. Hajdukiewicz JR, Vicente KJ, Doyle DJ, et al. Modeling a medical environment: an ontology for integrated medical informatics design. Int J Med Inform. 2001; 62(1):79–99.
    8. Jones G. EMR to EDC for RWE. May, 2017.
    9. Adeleke IT, Adekanye AO, Onawola KA, et al. Data quality assessment in healthcare: a 365-day chart review of inpatients’ health records at a Nigerian tertiary hospital. Journal of the American Medical Informatics Association : JAMIA. 2012; 19(6):1039-1042.
    10. Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group; 2001.
    11. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013; 20(1):117–21.
    12. Ross MK, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook of Medical Informatics 2014; 9(1):97-104.
    13. Califf RM, et al. Transforming evidence generation to support health and health care decisions. N Engl J Med 2016; 375:2395-2400

    MarksMan Healthcare

    , , , , ,

  • How Strong is the Evidence for Drugs Receiving FDA Accelerated Approval?

    How Strong is the Evidence for Drugs Receiving FDA Accelerated Approval?

    A recently published study by Dyer O (2017) exposed major drawbacks in the accelerated approval process of some drugs available to the American patients without any stringent clinical evidence of their benefits. (1) Drugs receiving fast track approvals from the US Food and Drug Administration (FDA) often rest on a weak evidence base, says research that examined over 7000 clinical studies conducted with more than 37 drugs that received such approvals between 2000 and 2013. Researchers from the London School of Economics and Political Science (LSE) and the United States say that many US patients with serious illnesses are being treated by drugs which have questionable data. (1,2)

    Although, drugs eligible for accelerated approval are assessed for their probable clinical benefits, the slab for their market entry is far lower than those receiving regular approval. The potentially promising drugs can receive marketing authorization based on surrogate measures that are easy to obtain, rather than clinically meaningful outcomes, with the help of FDA’s accelerated approval process. The aforementioned study1 is the first of its kind worldwide that systematically evaluated more than 7000 clinical studies on drugs receiving accelerated approval by the FDA; and the shortcomings were due to the FDA introducing more flexibility to its evidence standards over the past three decades. The evidence ultimately accrued on the drugs getting ‘accelerated approval’ has major flaws and is inadequate to address the information needs of patients and doctors, and other decision makers in healthcare systems. (3)

    The key findings of this study include: (1)

    • Randomized trials, the gold standard of evaluating clinical effectiveness, comprised only a small minority of existing evidence;
    • The FDA approval excluded the therapeutic areas in about one-third of randomized trials; out of these, less than half evaluated the therapeutic benefits of these drugs but used them instead as common backbone treatments;
    • Drugs receiving faster approval were frequently tested simultaneously in different therapeutic areas;
    • Most drugs did not show substantial time lag that was apparent between the average start date of trials evaluating their effectiveness and their use as background therapy;

    However, on a flip side, some in the industry believe that this is not news. Post-marketing studies are conducted in only two-thirds of cases and are usually followed with a median delay of 4 years. Accelerated approval is often associated with unjustifiable delays in market withdrawal, and even in drug-related deaths. (4) For lack of efficacy, the process is even slower; for instance, drotrecogin alpha was not withdrawn for 10 years after initial approval and bevacizumab was approved for metastatic breast cancer in February 2008 under the FDA accelerated program and the license was not withdrawn until November 2011. (5) No one can justify this delay by the system for being wrong for so long. FDA along with the European Agency (EU) approves the drugs as quickly as they are slow for withdrawal. (6)

    Collective evidence on drugs receiving accelerated approvals has major limitations. The majority of clinical studies with these drugs are small and non-randomized, and about one third are performed in disapproved areas, typically alongside those conducted in approved areas. Most randomized trials that include such drugs eligible for accelerated approval are not proposed to directly evaluate their clinical benefits but, in fact, to incorporate them as standard treatment. (2)

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. Dyer O. Drugs with FDA accelerated approval often have weak evidence, study finds. BMJ 2017 Jun 14; 357:j2905.
    2. Naci H, Wouters OJ, Gupta R, et al. Timing and characteristics of cumulative evidence available on novel therapeutic agents receiving Food and Drug Administration accelerated approval. Milbank 2017; 95(2):261-90.
    3. Major flaws in US drugs with ‘accelerated’ approval, research suggests. June 2017.
    4. Braillon A, Menkes DB. Balancing accelerated approval for drugs with accelerated withdrawal. JAMA Intern Med 2016; 176:566-7.
    5. Accelerated approval of drug on weak evidence is not the worst. BMJ News. June 2017. Accessed on 29th January, 2018.
    6. Bolland MJ, Grey A. Ten years too long: strontium ranelate, cardiac events, and the European Medicines Agency. BMJ 2016;354:i5109.

    MarksMan Healthcare

    , , , , ,

  • Whose Stent is it Anyway?

    Whose Stent is it Anyway?

    Bioresorbable stents have effectively been pulled from the European market by Abbott and will now be available only in “clinical registry setting at select sites/institutions” in Europe, where they will be monitored till a review in 2018. India has been among the largest markets for these stents in recent years. The development comes in the wake of several studies showing that bioresorbable cardiac stents are not only not superior to existing drug eluting stents (DES), but might even have worse outcomes in some ways. No further BVS (bioresorbable vascular scaffolding) stents will be provided to non-registry sites after 31st march 2017 and these sites have been instructed to cease implants and existing inventory will be removed,” stated an advisory from Abbott, which is “working jointly with the European Regulatory Agencies” to address concerns of increased risk of stent thrombosis and longer duration of use of blood thinners for those implanted with BVS compared to those with DES. These were the risks the studies had highlighted.

    This is a new addition to the whole stent controversy which is doing rounds in the healthcare industry since quite a while now. In February 2017, the National Pharmaceuticals Pricing Authority (NPPA) capped prices of stents according to quality: bare metal stents to cost no more than ₹ 7,260, while drug eluting stents and biodegradable stents were capped at ₹ 29,600. The ceiling prices came into effect immediately and were also applicable to all stocks in the trade channel. The government also stipulated that a manufacturer intending to stop production will have to inform the NPPA at least six months in advance.

    While many cardiologists thought of this move to be good, it certainly needed more thought. The move sparked panic in the city’s medical circles. The problem being the doctors have no alternative now because Abbott is the only supplier of bioresorbable stents in India. Over 8000 bioresorbable stents are used in India annually, over a thousand of them in Mumbai alone. The decision to use a particular stent involves the life and death of the patient on the angiography table. According to industry officials, four or five types of drug eluting stents are available in the market that are priced based on the innovation and technology. Advanced stents are more expensive than the base products. By no means can we underplay the fact that certain blockages call for good deliverability of the stents and therein the use of appropriate stents. Labeling them all into one category will kill the scope for further newer innovations.

    After the government’s decision to slash prices and enforce a single ceiling price for all the different variants of drug-eluting stents, manufactures and distributors started pulling off the high-end stents across the country. This is because manufacturers who took back their stock for re-labeling of prices did not return them. Doctors said the shortage inconvenienced a small group of patients with complex blockages in the artery. Scores of angioplasties in Mumbai were put on hold after Abbott Pharma withdrew bioresorbable stents from all hospitals, claiming it could not afford to sell the stents at prices mandated by the NPPA when they reduced prices of stents by over 75%. Before the cap on prices, Abbott used to sell bioresorbable stents at around Rs 1.9 lakh a piece.

    In July 2016, the health ministry included stents in the National List of Essential Medicines (NLEM) after the court demanded action on a public interest petition filed by advocate Birender Sangwan who sought price control on stents. The petitioner alleged that the government and the NPPA are being “insensitive and irresponsible” towards the people by not taking any steps to fix the price of the medical device which is allegedly being sold at high price in the country.

    While the maximum retail price (MRP) of a stent manufactured abroad and made in India could be similar, starting anything at over Rs 1 lakh, the cost to the patient could vary by a wide margin. While there is an MRP in the Indian market, at times stents are sold to hospitals at prices much lower, especially in the case of domestically manufactured stents. The Delhi high court had directed the central government to fix the MRP and a ceiling price by March 1, 2017, for coronary stents, used to treat narrowed or weakened arteries in patients.

    Additionally, health activists started debating if profit-driven medical institutions will actually pass on the benefit to patients, or find ways to recover the considerable cost difference from them. Despite the NPPA order, activists opined that the benefits being successfully passed on to patients depend on awareness. They believe that unnecessary angioplasties have been common but may increase due to the price control.

    As per a rough estimate, about 15% of the patients a cardiologist sees in his OPD would need an angioplasty, but in most corporatized hospitals, doctors are actually given targets of 40% for conversion. It won’t be surprising if hospitals hike costs of other disposables required in angioplasties like the balloon, wire, connectors and special syringes, and increase hospital stay cost. There is also a fear that more stents would be used on a patient. The lack of standard treatment protocols means there is always a risk of un-indicative procedures being performed. Such malpractices are already there due to commercialization of the medical field. The NPPA order is laudable, but when the government regularizes one thing, the industry discovers another way of making profits; and with regulation of stent prices, there could be more push towards even bypass surgeries. The answer to this problem lies in awareness. If something has been brought under price control, it does not mean it will be of bad quality. Consumers need to understand this and question each and every part.

    With the entire wrangle over the stent prices and stent shortage in the market eventually de-prioritizing ‘patient care’, one question keeps hovering over us. Whose stent is it anyway??

    Become a Certified HEOR Professional – Enrol yourself here!

    MarksMan Healthcare

    ,

  • Database Use in Outcomes Research Studies

    Database Use in Outcomes Research Studies

    Clinical research includes the traditional prospective clinical trials, cohort studies, and case-control studies, while outcomes research (OR) has now solidified itself as a separate class of clinical research.

    OR is an evolving and critical part of health sciences research due to its focus on the improvement of patient care in the practice setting. Evidence included in OR spans all available clinical information, from data generated by randomized controlled trials to real-world clinical, economic, humanistic, patient-reported, and patient satisfaction data. Comparative effectiveness research (CER) is a branch of OR that compares the overall benefit or cost of two active interventions. The use of databases has emerged as a valid, rigorous, and efficient source of data for CER.

    One increasingly emergent trend in OR is the evaluation of existing information that has been captured and stored for the primary purpose of processing payment claims for health care services rendered. The employment of these secondary or administrative databases is a distinctly useful method. Large-volume and clinical and administrative databases are used to study regional practice pattern variations, health care disparities, and resource utilization. Generally, large-volume databases can be broadly categorized as either administrative or clinical based on where the data are derived. Administrative databases are largely based on billing information while clinical databases are populated with defined patient information. The geographic catchment areas of individual databases may also vary. They may be national, state-based, or limited to specific geographic areas. All patients in a particular catchment area may be included, or limited to a representative sample.

    As their names suggest, the primary purposes of research and administrative databases are different. Typically, it can be assumed that information provided by a research database would be more in-depth and valid than that provided by administrative database. However, researchers may find it more difficult to obtain access to research databases than to administrative claims databases. Moreover, most research databases are specific to a single’ disease category, which limits their applicability for research in other disease categories. These factors highlight the practical utility of administrative claims databases in outcomes research.

    Popular databases include the U.S. Health Care Financing Administration’s Medicaid and Medicare databases, the National Ambulatory Medical Care Survey, the National Health Interview Survey, and the Healthcare Cost and Utilization Project; which are relatively inexpensive and offer several advantages. Decision makers often want answers to questions about health care delivery in a relatively short time, too short for long-term prospective studies. Administrative patient databases are appropriate tools for this type of research. They offer ready access to the longitudinal data needed for retrospective studies on medical practice issues that would otherwise require lengthy prospective studies, especially in assessing the effectiveness of interventions in managing chronic diseases.

    Many of the benefits of these databases, such as greater efficiency, lower expense, and reduction of bias have been described previously and often outweigh their limitations, including incomplete or missing data and a lack of integration of different types of claims data (medical versus mental health versus prescription drug claims). This positive balance of attributes affords investigators a tool with great versatility and convenience. The lack of head-to-head active treatment comparison trials, especially with interventions past their patent expiration and many newer agents, can be addressed by evaluating multiple clinical and economic effects of interventions in a real-world environment using database studies. Evolving research design and analysis techniques for databases and the minimization of bias coupled with the rapid availability of results collected from patient encounters in a realistic setting have all combined to increase its popularity and acceptance in the scientific community.

    These administrative and clinical databases serve as well-powered tools to evaluate regional treatment variation and disparities in care. Use of population-based data permits investigations with adequate power that would not be possible with more limited single-site clinical data. Careful study design, appropriate database selection, and rigorous analyses allow investigators to answer key clinical questions in research.

    Become a Certified HEOR Professional – Enrol yourself here!

    MarksMan Healthcare