Contact Us

Home

About us

Services

AI Excellence

case studies

publications

HEOR Certificationheor Certification

AI for Medical Writing

Contact

  • Digital Endpoints in Value Generation for Health Technologies

    Digital Endpoints in Value Generation for Health Technologies

    The inception of digital endpoints in clinical trials holds great potential to transform research methodology, which is characterized by easy and perfect data collection. This will increase the development of life-saving therapies, and can also enhance patient engagement. The newly introduced digital endpoints are driven by advancements in technology, including the miniaturization of sensors, enhancement in data analytics, the wider implementation of wearable devices and mobile health applications by patients and healthcare providers.[1]

    Digital endpoints are used as essential tools for evaluating the effectiveness and value of novel treatments and interventions. The drawbacks of conventional endpoints may be addressed by digital endpoints, which are characterized by sensor-generated data from locations other than clinical settings. Digital endpoints offer a novel approach to measuring health outcomes, providing a more granular and giving a more detailed and continuous picture of patient health. Traditional clinical endpoints like mortality rates or disease progression markers frequently only offer a glimpse of the state of health at specific points in time. On the other hand, digital endpoints provide an ongoing, real-time data stream that allows for a more sophisticated comprehension of health trajectories and the impact of interventions over time. As a consequence, these digital endpoints can be valuable for both evaluating diseases and the health technology that generates the endpoints.[1,2]

    Health technology assessments (HTAs) and clinical trials may gain benefit from the integration of digital endpoints in a number of ways, including better self-management, enhanced patient participation, and improved patient engagement through the provision of real-time health feedback.  In addition, eliminating recall bias through continuous evaluation, it enhances the quality of data by facilitating remote, patient-centered data gathering, improving treatment efficacy assessments, and producing more accurate results. Additionally, by facilitating real-time patient regimen monitoring, which enhances outcomes and lowers side effects, digital endpoints promote medication safety and adherence. Furthermore, digital endpoints can drastically reduce the operational costs related to clinical trials and HTAs by automating data collecting and reducing the need for in-person visits.[3,4]

    Despite its promise, using digital endpoints presents several difficulties, such as safeguarding data security and privacy, particularly in light of the rise in cyberattacks. Furthermore, it is critical to have standardized and verified procedures to ensure that digital endpoints accurately reflect health outcomes and remain consistent across research. Regulatory approval continues to be a major obstacle, and the relatively low adoption of digital endpoints among large pharmaceutical companies can be attributed to both difficult regulatory constraints and inadequate finance.[5]

    Regulatory agencies are working hard to create frameworks for the use of digital endpoints in research and HTA, keeping in mind the potential they provide. A road map for using digital health tools to gather clinical data and identify new endpoints in drug development may be found in the USFDA’s draft guidance on “Digital Health Technologies for Remote Data Acquisition in Clinical Investigations.” Similarly, a framework for using digital technology for data collection and management is established by the EMA’s recommendations on computerized systems and electronic data in clinical trials.  Despite the fact that these recommendations open the door to more effective and patient-centric medication development, difficulties still exist. Careful considerations must be given to data privacy and security, standardization and validation of digital endpoints, and overcoming existing regulatory hurdles.[6,7]

    To sum up, digital endpoints are a big step forward in determining the value of health innovations. They have the ability to revolutionize our understanding of and ability to enhance health outcomes by providing a more thorough and continuous picture of patient health. To fully realize this promise, the related difficulties must be resolved, and it is imperative that digital endpoints be utilized sensibly and efficiently throughout the healthcare ecosystem.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Landers M, Dorsey R, Saria S. Digital endpoints: definition, benefits, and Current barriers in accelerating development and adoption. Digit Biomarkers. 2021;5(3):216–223. doi:10.1159/000517885
    2. Clinixir. Unleashing Potential for Digital Endpoints: How They Can Revolutionize Clinical Research [Internet]. Available from: https://www.clinixir.com/blog/unleashing-potential-for-digital-endpoints-how-they-can-revolutionize-clinical-research/
    3. Bian S, Zhu B, Rong G, et al. Towards wearable and implantable continuous drug monitoring: a review. J Pharm Anal. 2021;11(1):1–14. doi: 10.1016/j.jpha.2020.08.001
    4. Al Meslamani AZ. The long-term clinical impact of digital endpoints and biomarkers in data collection. Expert Rev Pharmacoecon Outcomes Res. 2024;1–3. doi:10.1080/14737167.2024.2320233
    5. Breaking the mold: How digital endpoints could transform clinical trials [Internet]. Available from: https://www.aissel.com/blog/how-digital-endpoints-could-transform-clinical-trials
    6. Current references on clinical endpoints derived from Digital Health Technologies [Internet]. Available from: https://www.efpia.eu/media/676660/efpia-digital-endpoints-reference-documents.pdf
    7. Guideline on computerised systems and electronic data in clinical trials [Internet]. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf

    MarksMan Healthcare

    , , , ,

  • Comparative Effectiveness in Real-World Settings through Pragmatic Clinical Trials

    Comparative Effectiveness in Real-World Settings through Pragmatic Clinical Trials

    Randomized controlled trials (RCTs) are the mainstay of clinical research; it is estimated that about 18,000 RCTs are published each year. However, traditional RCTs usually take a long time to complete, are expensive, and the results are challenging to generalize to the real-world since they are derived under ideal conditions with strict inclusion and exclusion criteria. This brings in an additional layer of complexity for decision-making by the healthcare stakeholders and reimbursement authorities. With an intention to resolve this challenge, fuelled by the increasing global shift towards personalized medicine and value-based payment models, new methods for generating efficacy and safety evidence of interventions in real-world settings are continuously sought after.[1,2]

    This has been a major driver for a rapid increase in interest in comparative effectiveness research (CER), which aims to compare the benefits, risks, and sometimes costs of alternative healthcare interventions (medicines, medical devices, procedures and health services) in real-world settings. CER aims to assist consumers, clinicians, purchasers, and policymakers in making informed decisions, thereby improving healthcare at both the individual and population levels.[3]

    CER was brought into spotlight with the introduction of the American Recovery and Reinvestment Act in 2009 which provided support of $1.1 US billion over 2 years for conducting CER. This stimulated an increase in the observational studies in the short run and conducting RCTs in the long run.[4] In 2020, the Patient-Centered Outcomes Research Institute (PCORI) had invested nearly $2.6 billion in more than 700 patient-centered CER studies in the USA.[5] In 2011, it was estimated that CER may contribute to a $31.6 billion reduction in research and development costs over a 10 year period by improving market access and reimbursement from private insurers.[6]

    CER involves non-inferiority trials between two interventions having similar therapeutic effects differing in other aspects relevant to stakeholders like costs, adverse effect profile, and route of administration. Among several trial designs, key trial design proposed in CER is Pragmatic Clinical Trials (PCT). PCTs are in fact RCTs, conducted in a real-world setting: the evidence generated through PCTs can be translated into patient care more efficiently and with a better generalizability. While traditional RCTs use a placebo or well-controlled alternative intervention in a tightly controlled study setting, PCTs are intended to maintain the internal validity of RCTs and maximize the external validity (generalizability and applicability). PCTs are designed and implemented in ways that would better address the demand for evidence about real-world risks, and benefits for informing clinical and health policy decisions.[4,7]

    PCTs are gaining traction in CER due to their potential to efficiently generate evidence to inform real-world health care decisions by embedding research into routine care with the goals of implementation research. A notable example of CER through PCTs is the ALLHAT trial reported in 2004, which concluded that thiazides are as effective as ACE inhibitors in the management of Hypertension.[8] Similarly, the 2006 CATIE trial reported that atypical antipsychotics are ineffective compared to placebo in elderly patients with dementia.[8]

    Despite its advantages, the “embedded” nature of PCTs (i.e RCTs embedded in real-world setting) faces ethical and regulatory challenges. Existing GCP guidelines intended for traditional RCTs are insufficient in the areas of PCTs, and appropriate reforms are needed that are relevant to conduct PCTs.[9] The design and quality of a CER depend on the proper choice of the non-inferiority margin. However, defining the non-inferiority margin can be complex and quite challenging. Attrition bias adds to these complexities. These concerns are being sorted out with the evidence from previous studies, preliminary data, and/or clinical judgment that are very helpful in allowing the trialists to make reasonable assumptions about an anticipated effect of the reference treatment.[10,11]

    Challenges also exist in sustaining the behavioural change following decisions from CER. For example, based on CER, a clinical decision was made not to use stents for stable angina: this reduced stent implants by 13% in the US for 4 years; however, by 2009, the number of implants returned back to previous levels.[12]

    Development of proper regulatory standards can enable the realization of the full potential of CER conducted through pragmatic trials to fill the research-practice gap in healthcare decision-making, reduce variability in clinical practice, and determine the high-quality care for all patients. Indeed, CER has the potential to provide the best possible treatment choices to the patients and the healthcare providers.

    Become A Certified HEOR Professional – Enrol yourself here!

    References:

    1. https://www.asianhhm.com/healthcare-management/decision-based-evidence-making
    2. Alsop J et al. The mixed randomized trial: combining randomized, pragmatic and observational clinical trial designs. Journal of Comparative Effectiveness Research. 2016;5(6):569-579.
    3. Dang A, Kaur K. Comparative effectiveness research and its utility in In-clinic practice. Perspectives in Clinical Research. 2016;7(1):9.
    4. Mullins C et al. Generating Evidence for Comparative Effectiveness Research Using More Pragmatic Randomized Controlled Trials. PharmacoEconomics. 2010;28(10):969-976.
    5. https://www.pcori.org/news-release/pcori-board-approves-new-150-million-initiative-fund-large-scale-patient-centered-clinical-studies. 2020.
    6. https://www.kff.org/wp-content/uploads/sites/3/2011/05/cer_paper_final.pdf
    7. Chalkidou K et al. The role for pragmatic randomized controlled trials (pRCTs) in comparative effectiveness research. Clinical Trials. 2012;9(4):436-446.
    8. Schneeweiss S. Developments in Post-marketing Comparative Effectiveness Research. Clinical Pharmacology and Therapeutics. 2007;82(2):143-156.
    9. Mentz R et al. Good Clinical Practice Guidance and Pragmatic Clinical Trials. Circulation. 2016;133(9):872-880.
    10. Colditz G, Winter A. Clinical trial design in the era of comparative effectiveness research. Open Access Journal of Clinical Trials. 2014;:101.
    11. Siegel J et al. Comparative Effectiveness Research in the Regulatory Setting. Pharmaceutical Medicine. 2012;26(1):5-11.
    12. Kupersmith J, Ommaya A. The Past, Present, and Future of Comparative Effectiveness Research in the US Department of Veterans Affairs. The American Journal of Medicine. 2010;123(12):e3-e7.

    MarksMan Healthcare

    , ,

  • An Overview of Registry Studies and Considerations for Regulators

    An Overview of Registry Studies and Considerations for Regulators

    A patient registry has been defined as “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 a predetermined scientific, clinical or policy purpose.”(1) Patient registries provide an infrastructure for the standardized recording of data from routine clinical practice on individual patients classified by a characteristic or an event. These may include the diagnosis of a disease (disease registry), the occurrence of a condition (e.g., pregnancy registry), a birth defect (e.g., birth defect registry), a molecular or genomic or any other patient characteristics, or an encounter with particular healthcare service.(2, 3)

    Studies based on registries (registry studies) aim to investigate a research question using the infrastructure of new or existing registries for patient recruitment and data collection. The objectives of these studies may include epidemiological data collection, trend analysis, efficacy, safety, long term and short term outcomes of different treatment options, and patient-reported outcomes such as quality of life and patient satisfaction.(3) A registry study may implement primary data collection and/or secondary use of data collected in a patient registry for a different purpose from the given study. When ‘patient’ and ‘registry’ are used in combination (i.e., patient registry), they highlight the focus of the dataset on health information.(4)More importantly, such registry studies offer different stakeholders in the pharmaceutical industry (including prescribers, healthcare providers, regulators, payers, government, and also the patients) an additional layer of evidence beyond clinical trials, surrounding the efficacy and safety of drugs and medical devices. (1)

    Patient registries are particularly useful for generalizing the findings of clinical trials to populations not included in those trials. Additionally, they can also help evaluate the impact of physician decision-making and actions on care delivery. Registries are also increasingly being utilized for observational comparative effectiveness studies as they have proven to be cost-effective while providing useful information in comparison with the alternative methods.(2) Registry data have great potential to enhance the efficiency of clinical trials, making them less expensive, and speeding up the availability of new treatments to patients. Depending on the capabilities and characteristics (e.g., interoperability, connectivity, flexibility, sustainability), a registry can be used either as a) an observational data source for the generation of clinical evidence and hypotheses or b) an important reusable element of the clinical trial infrastructure for prospective randomized studies.(5) Information collected in clinical observational registries, such as demographics, medical history, diagnosis, and outcomes data, often overlap with clinical trial data. Therefore, integration of clinical trials within registries may offer opportunities to 1) Avoid data collection duplicity, 2) More efficient identification and recruitment of patients, 3) Faster database lock, 4) Faster critical decision-making, and 5) Reduction of clinical trial costs.(5)

    Considering the extent of impact that registry studies can have on the usage of drugs and medical devices, factors surrounding patient privacy, confidentiality, ethics, and other regulatory issues gain extreme significance. Regulatory oversight is essential to ensure that the outcomes of registry audits are done appropriately, avoiding misguiding claims of efficacy and/ or safety. Thus it becomes crucial for regulators to assess whether the historical evidence generated from registry studies demonstrates the reliability, robustness, and relevance to support regulatory decision-making. Additionally, the assessment of an existing registry can also be done to see if it contains the elements for a randomized clinical trial, as it can potentially provide high-quality evidence for regulatory decision-making.(5)

    On the other hand, while designing a new registry, there are a few general guidelines that need to be considered along with specific ones for a particular regulatory authority. The new registry must have the lowest possible barriers for inclusion, thus maximizing the inclusion of those having the disease/condition to be studied. Different treatment modalities, including drugs, biologics, devices, and combination products, should be considered. Identification of data elements should be performed on the basis of their clinical relevance while maintaining recognized standards and nomenclature. Data collection processes must be systematic, reproducible, reliable, and in accordance with informatics standards. The new registry design should be valid across multiple stakeholder analyses. Finally, a provision should be made to incorporate patient-reported information within the registry.(5)

    Appropriately and ethically conducted registry studies can therefore immensely contribute to medical evidence, and prudent regulatory oversight is essential to ensure that the quality of evidence coming from the registry studies is not compromised.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. R. E. Gliklich and N.A. Dreyer, “Registries for Evaluating Patient Registries: A User’s Guide,” Agency for Healthcare Research and Quality, publication No. 07-EHC001 (AHRQ, Rockville, MD, 2007).
    2. Gliklich R. Clinical Trials vs. Registries. April 2009. Available at: https://www.appliedclinicaltrialsonline.com/view/clinical-trials-vs-registries
    3. European Network of Centres for Pharmacoepidemiology and Pharmacovigilance. April 2022. Available at: https://www.encepp.eu/standards_and_guidances/methodologicalGuide4_3.shtml
    4. European Medicines Agency. Guideline on registry-based studies. (EMA/502388/2020) September 2020. Available at: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-registry-based-studies_en.pdf
    5. Clinical Trials Transformation Initiative (CTTI). CTTI Recommendations: Registry Trials. Available at: https://ctti-clinicaltrials.org/wp-content/uploads/2021/06/CTTI_Registry_Trials_Recs.pdf

    MarksMan Healthcare

    , ,

  • How Integration of Multiple Data Sources can Improve Patient Insights?

    How Integration of Multiple Data Sources can Improve Patient Insights?

    There are humungous quantities of data existing in healthcare; data from all kinds of sources, such as clinical, patient, payer, R&D, pharmacy as well as revolutionary technologies that are being quickly embraced, for e.g. data from wearable devices. According to a report by International Data Corporation (IDC), (1) the volume of healthcare data which was observed to be around 153 exabytes in 2013 is estimated to reach around 2,314 exabytes in the year 2020. Therefore, integrating data from all types of diverse sources and clinical systems is a fundamental challenge for any healthcare entity in order to enhance patient care and performance indicators. (2)

    It’s obvious that these huge amounts of health data are essential for betterment of both the cost as well as the quality aspects of care. Also, analyses of these data can provide significant insights for patients and researchers. However, methods to merge data from multiple formats and sources ranging across various systems used within clinics are still unclear. Data quality and accessibility provided by these systems can vary to a great extent. The healthcare industry has been traditionally observed to embrace new technologies; however, it lags behind while handling data, particularly data sharing and integration. To add to the practical challenges of data integration processes, compliance and capability to join forces with all the healthcare stakeholders also faces problems. As a consequence, data collection, storage, integration, and analysis make up for complicated processes. (2)

    There are some specific underlying concerns surrounding multiple, un-integrated data sources, viz. lack of broad view into enterprise-wide data as well as data standardization and governance, and matching patients to care events. Lack of broad view can impose challenges resulting in time consuming and expensive procedures during development of meaningful internal and external reports, like quality and patient safety regulatory and accreditation reporting. It may also hamper efforts to identify and prioritize opportunities to reduce costs, while improving care and patient experience. Lack of data standardization and governance can hamper performance of important analytics owing to multiple data sources, definitions and terms. Last but not the least, it is crucial to match patients accurately to their respective care events across multiple sites of care, which can be a complicated process. (3,4)

    There is no doubt that the Healthcare systems undoubtedly require effective data integration tools and greater level of flexibility when handling data, typically from multiple sources. The standards implemented in many countries recently have been intended for healthcare data integration and unification. For instance, in the USA the Health Information Technology (HITECH) Act (5) offers incentive payments to health care providers implementing certified EHR technology while showing meaningful use of that technology. HIPAA standards provide healthcare data protection; while HL7 standards allow clinical and administrative data communication between software applications used by various healthcare providers. (6)

    In order to gain patient insights, integration of data from multiple sources can prove to be beneficial. One way to facilitate data integration can be incorporating data warehouses [enterprise data warehouses (EDWs)], which can facilitate easy data mining in case of faster, major data initiatives. These methods can pull in and push out data with just one interface. Furthermore, data governance policies focusing on data standardization, advances in data reporting and further education and communication need to be in place in order to make changes in how data is to be collected, defined, and consumed. By integrating health data with financial and cost data to track patient encounters across multiple care locations and information systems, it is easier for health systems to compare patient quality and cost, i.e. comprehending the exact process of ‘value’ delivery. This insight is the difference between surviving and thriving in the new value based purchasing environment. (4)

    Clinical data integration from multiple sources can provide a wide-ranging perspective across care delivery systems. Health systems can easily carry out reporting while employing quality improvement initiatives, such as analytical care variation and measuring implementation of evidence-based guidelines. (4)

    To sum it all up, multiple data integration can obviously facilitate electronic exchange of information, while also reducing the costs and intricacies of building interfaces between different systems; thus proving valuable patient insights. The foundation of the healthcare industry’s data-sharing conundrum is data interoperability. Genuinely integrated systems must be easily understood by users, i.e. these systems must be able to exchange data and consequently put it forward through inclusive and user friendly interface.

    Become an Certified HEOR Professional – Enrol yourself here!

    References 

    1. Corbin K. How CIOs can prepare for healthcare ‘data tsunami’. December, 2014.
    2. Healthcare data integration: How to combine data from multiple sources. 
    3. Managing the integrity of patient identity in health information exchange. American Health Information Management Association. 2009. 
    4. Turning Data from Five Different EHR Vendors into Actionable Insights. Health Catalyst.
    5. Health Information Technology (HITECH Act). 2009. 
    6. Summary of the HIPAA Privacy Rule. 

    MarksMan Healthcare

    , , , , , ,

  • How Well are we Able to Quantify ROI from Patient Centric Activities?

    How Well are we Able to Quantify ROI from Patient Centric Activities?

    In recent years, patient-centric initiatives are becoming key factors among healthcare companies, as they are increasingly becoming aware of the fact that the key to growing business and improving customer health is to better focus on the needs and concerns of the patient, rather than attending to just the product approval. The objective behind patient-centric drug development and other associated initiatives is to involve patients and the healthcare community as partners in order to bring about a sense of ownership in the success of new medical treatments. (1,2) There are four core principles that frame the growing arsenal of patient centric initiatives: (3)

    1. Relevance: Unmet medical needs identified in collaboration with and based on input from patients and their healthcare support network
    2. Pragmatism: Agendas and clinical trial designs recognised and accommodated in real-life patient and healthcare community needs and experiences
    3. Feasibility: The burden of study participation minimised and supported by initiatives that improve convenience
    4. Interactivity/Participation: Patient’s voice responded with the support from patient community, and giving the patient community an opportunity to be actively involved throughout the research process by amplified planning and execution

    Adherence to the above principles facilitate patient-centric drug development to encourage the sharing of important information and drug development risk among a broader community of external partners e.g., academic and basic research groups, co-development sponsors, development operations alliances, and patient advocacy groups. (3)

    A growing number of researchers are adapting to patient-centric initiatives across multiple studies, which can eventually be used while centrally monitoring and coordinating activities. This can further promote harmonization and can also better assist in communicating lessons learned from earlier implementations and from peer companies. Such activities can also help in deriving consensus metrics to evaluate the impact of various initiatives on organizational and study-level processes and performance. (3,4)

    Return on investment (ROI) expectations are required to consider a reasonably long-term view. This is because researchers need enough time to collect experience with patient-centric initiatives, learn from mistakes while continuously processing their use. Ideally, target measures from multiple representative studies should be gathered two to three years prior to and after the implementation of initiatives. Stakeholders use some key implementation and ROI metrics to measure three broad areas, viz. reach; patient/study volunteer feedback; and performance. (3)

    • Reach measures (e.g., number of pilot initiatives, number of planned initiatives, etc.) are usually quantitative, having an aim to assess the extent of adoption and usage within organisations, along with the number of patients and study volunteers who have participated in a given initiative.
    • Patient/study volunteer feedback measures (such as ratings, etc.) are more often qualitative in nature; however, several organisations do implement a few quantitative feedback measures as well. Qualitative measures examine the subjective reports of satisfaction; sense of involvement in a given study or in association with a specific initiative, and the perceived relevance of specific clinical trials; while quantitative measures determine efficiency of patient/study volunteer participation in facilitating change. (3,4)
    • Performance measures (viz. screen failure rates, number of procedures per visit or protocol amendments, etc.) are quantitative and they largely compare studies, whether or not they include the usage of patient-centric initiatives or not. (3)

    We feel that it is too early to conclude on this aspect, as there is inadequate data demonstrating the extent and impact of patient-centric initiatives, across the industry. Needless to mention that patient-centricity movement is certainly inspiring the drug development enterprise to challenge and transform the traditional drug development prototype by putting the patient at its core.

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. Lopez Kunz B. Trial Return On Engagement: Quantifying Benefits Of Patient-Centric Initiatives. Clinical leader. June, 2017. 
    2. A patient-centric approach: Patient-centric initiatives in drug development. March, 2017.
    3. Getz KA. ROI for patient-centric drug development. Applied Clinical Trials. August, 2015. 
    4. Gets K. The transformative promise of patient centric R&D. May, 2015.
    5. Markowitz FE. Involvement in mental health self-help groups and recovery. Health sociology review : the journal of the Health Section of the Australian Sociological Association 2015; 24(2):199-212.

    MarksMan Healthcare

    , , ,

  • How Building a Culture of Measurement in Healthcare can Improve Patient Outcomes?

    How Building a Culture of Measurement in Healthcare can Improve Patient Outcomes?

    The healthcare industry is challenged with administrative and regulatory intricacies that make achieving the healthcare objectives, such as better patient outcomes and reduced costs, difficult. Difficulties faced while improving patient outcomes are predominantly taxing, since health systems measure and report thousands of outcomes annually. (1) In addition, healthcare industry is saturated with the need for improved quality and safety programs. (2) Quality healthcare refers to “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge”. (3)

    Moreover, healthcare is moving from the ‘volume of services delivered’ to the ‘value’ created for patients, which is defined as the outcomes achieved relative to the costs. (4) However, this shift has been slow, to a certain extent, owing to the restricted measurement of outcomes that matter to patients, except survival. In addition, ‘death’ is an unusual outcome in many situations, wherein the measurement fails to differentiate excellent from merely capable providers. (5)

    “To Err Is Human”! Also, most of the medical errors originate from defective systems and processes, not individuals. These incompetent and inconsistent processes, changing patient cases and health insurance, varying degrees of healthcare providers’ education and expertise, and various other aspects add up to healthcare complications. Many believe that the healthcare industry today operates at a lower level than it can and should; however, it should focus on more comprehensive goals, such as efficient, safe, patient-centric, well-timed, and unbiased care delivery. The efficacy and safety intentions can be delivered through process-of-care measures, assessing whether the processes achieve the desired goals while avoiding those that prompt the harm. Measurement of healthcare quality determines the effects of healthcare on desired outcomes and also assesses the degree of adherence of healthcare to processes that are evidence-based or those adhering to the general consensus that is in line with patient preferences. (6)

    There are many outcome measures, ranging from changes in blood pressure in patients with hypertension to patient-reported outcome measures (PROMs). However, there are seven main outcome measures which are used by healthcare institutions and other stakeholders, viz. mortality, rehospitalizations, safety, efficacy, patient experience, timeliness of care and efficient use of medical imaging and other markers. Having said that, processes to accomplish the outcomes are as important as is achieving those outcomes. Process measures determine provider productivity and adherence to standards of recommended care. For example, to reduce the incidence of skin breakdown, a particular health system may apply the process measure to perform the risk evaluation by using Barden Scale for reducing pressure ulcer risk in all the appropriate units in the organization/institution. If health systems are focused on an outcome too much, they may lose track of the process. (1)

    Apart from all the shortcomings, the primary goal of the healthcare systems and organizations globally is to improve patient outcomes. However, this improvement cannot take place without efficient measurement. As all the stakeholders work attentively to achieve the composite healthcare goals, they need to prioritize the outcomes measurement tools: transparency, integrated care, and interoperability. When used along with each other, these tools can improve and maintain outcomes measurement efforts by generating a data-driven culture that embraces data transparency and an integrated care environment to treat patients. This also improves critical care transitions and interoperable systems, which facilitates the perfect exchange of outcomes measurement data between clinicians, departments, and hospitals. (1)

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    (1) Tinker A. The top 7 outcome measures and 3 measurement essentials.
    (2) National Healthcare Quality Report. Rockville, MD: Agency for Healthcare Research and Quality; 2006. 
    (3) Lohr KN, et al. A strategy for quality assurance in Medicare. N Engl J Med. 1990; 322:1161–71
    (4) Porter ME. What is value in health care? N Engl J Med 2010; 363:2477-2481
    (5) Porter ME, et al. Standardizing patient outcomes measurement. N Engl J Med 2016; 374:504-506
    (6) Hughes RG. Tools and Strategies for Quality Improvement and Patient Safety. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr. Chapter 44.

    MarksMan Healthcare

    , ,