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  • Exploring Patient Registries: Managing Market Access With Real World Evidence

    Exploring Patient Registries: Managing Market Access With Real World Evidence
    Exploring Patient Registries Managing Market Access With Real World Evidence

    In the increasingly data-driven world of healthcare, patient registries have emerged as powerful tools for managing market access through the application of real-world evidence (RWE). These registries, essentially databases that collect information about patients’ demographics, clinical history, treatment outcomes, and other pertinent details, provide a treasure trove of insights that can bridge the gap between clinical trials and everyday medical practice. The use of patient registries in gathering RWE is transforming how pharmaceutical companies, healthcare providers, and policymakers evaluate and manage the accessibility and effectiveness of medical treatments and interventions.[1]

    Patient registries provide a comprehensive view of how treatments perform outside the controlled environment of clinical trials. Clinical trials, while essential for establishing the efficacy and safety of new therapies, often have limitations, such as restrictive inclusion criteria and relatively short follow-up periods. In contrast, patient registries encompass a broader patient population, including those with comorbidities and varying adherence levels, thus reflecting real-world scenarios more accurately. This inclusivity makes registries a valuable source of RWE, which is increasingly recognized by regulatory agencies and payers as critical for assessing the long-term value and impact of medical interventions. Regulators also view the use of registries as a key tool for generating RWE, supporting decision-making processes across areas such as post-market surveillance, submissions for regulatory approval, and health technology assessments (HTAs).[1]

    One of the primary benefits of utilizing patient registries is the ability to generate evidence on long-term safety and effectiveness of treatments. This ongoing evidence collection improves patient trust in the intervention, thereby increasing patient access. For instance, post-marketing surveillance through patient registries can uncover adverse effects or benefits that may not be apparent during pre-approval clinical trials. This continuous monitoring helps in ensuring that the treatments remain safe and effective once they are in widespread use, supporting regulatory decisions and guiding clinical practice. Consequently, this fosters greater confidence and willingness among patients to adopt new treatments, enhancing their accessibility and uptake in the general population.[2]

    Patient registries also play a pivotal role in market access by demonstrating the value of new treatments to payers and health technology assessment (HTA) bodies. The data derived from these registries can support health economic evaluations, such as cost-effectiveness and budget impact analyses, which are essential for reimbursement decisions. By providing robust evidence on the real-world effectiveness and safety of treatments, registries help build a compelling case for their adoption and coverage. This is particularly important in an era where healthcare budgets are constrained, and there is increasing scrutiny over the allocation of resources.[3]

    Despite their benefits, patient registries face challenges that must be acknowledged to provide a balanced perspective. Issues such as data quality inconsistencies, difficulties in patient recruitment, and regulatory hurdles can hinder their utility. For example, inconsistent data collection processes may impact the reliability of insights, and recruitment barriers can limit the diversity of patient populations within registries. Addressing these challenges requires standardizing methodologies, fostering collaboration among stakeholders, and ensuring compliance with evolving regulatory frameworks. Additionally, emerging trends such as the integration of digital health technologies and artificial intelligence (AI) are redefining the capabilities of patient registries. AI, for instance, can analyze large-scale registry data to identify patterns, predict treatment outcomes, and optimize resource allocation, enhancing the potential of RWE to inform decision-making.[4]

    Collaboration among stakeholders is critical for the success of patient registry initiatives. Engaging a diverse group of participants, including patients, healthcare providers, researchers, and industry partners, ensures that registries are comprehensive and relevant. Leveraging technologies such as electronic health records (EHRs) and mobile health applications can streamline data collection, improve accuracy, and enhance data utility. These collaborative and technological advancements position registries as powerful tools for addressing unmet medical needs, optimizing healthcare delivery, and achieving equitable access to treatments.[4,5]

    In conclusion, patient registries stand as pillars in the contemporary healthcare landscape, offering a bridge between clinical trials and real-world practice. Their role in managing market access through the application of RWE is indispensable, shaping how pharmaceuticals are evaluated, accessed, and integrated into healthcare systems. With their ability to capture the intricacies of patient experiences over time, these registries empower stakeholders to make informed decisions, driving advancements in healthcare delivery and ensuring that groundbreaking therapies reach those in need. By addressing challenges, embracing innovation, and fostering collaboration, patient registries will continue to play a transformative role in optimizing patient care and healthcare resource utilization.

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    References:

    1. Trotter JP. Patient registries: a new gold standard for “real world” research. Ochsner Journal. 2002 Sep 21;4(4):211-4.
    2. Reid CM. The role of clinical registries in monitoring drug safety and efficacy. Heart, Lung and Circulation. 2015 Nov 1;24(11):1049-52.
    3. Blommestein HM, Franken MG, Uyl-de Groot CA. A practical guide for using registry data to inform decisions about the cost effectiveness of new cancer drugs: lessons learned from the PHAROS registry. Pharmacoeconomics. 2015 Jun;33:551-60.
    4. Gliklich RE, Dreyer NA, Leavy MB. Patient registries. InRegistries for Evaluating Patient Outcomes: A User’s Guide [Internet]. 3rd edition 2014 Apr. Agency for Healthcare Research and Quality (US).
    5. Daugherty SE, Lee SB, Nowell B, Peay H, Solomon D, Valbrun TG, Velentgas P, Whicher D. The increasing focus on the patient in patient registries. In21st Century Patient Registries: Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition, Addendum 2018 Mar. Agency for Healthcare Research and Quality (US).

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  • Impact of Technological Advances on Drug Development and Decision Making

    Impact of Technological Advances on Drug Development and Decision Making

    Healthcare management is a combination of information science, computer science, information technology and healthcare. It incorporates resources, devices, and methods for optimising the acquisition, storage, retrieval, and use of data in health and biomedicine. This includes not only computers, but also clinical guidelines, medical terminologies as well as information and communication systems. Research and development efforts within the healthcare industry and ever increasing technological advancements over the last two decades have brought about momentous improvements in the quality of medical services to the patients. Developed countries are contributing a lot of resources in order to improve their healthcare systems as well as their integration with information technology. (1)

    It’s a universal fact that much of the recent progress in the medical/healthcare field can be specifically attributed to technological advancements. The advantages of technology and innovation in healthcare roughly span across quality of human life, healthcare jobs and opportunities, and the global economy. It is evident that interest in medical innovation is not to slow down, especially as new investments continue to fuel the private sector. Moreover, the decline in preventable death cases as well as a general improvement in patient well-being make for an important technological advantage. Additionally, time for treatment and recovery has been substantially reduced. (2)

    It’s a no-brainer that private competition is driving innovation to new heights as more and more useful gadgets are hitting the healthcare market. Most smartphones, tablets and other similar devices already help us monitor our health. With medical information available from a wide range of sources, modern patients are ‘smart individuals’ who access different apps on their gadget to keep a track of their health parameters. Consequently, the demand for more accurate and dedicated health-tracking devices has created highly specialised niches; for e.g. wearables. Wearable technologies are usually clothing items or accessories, which integrate various practical functions and features. For example, inexpensive, advanced yet comfortable wearables are available that will monitor a person’s vitals for any signs or complications related to heart failure, breathing problems, UTI or diabetes. These can notify the concerned healthcare provider at the first sign of a medical problem.(2) Another innovation is telemedicine, which merges advanced telecommunication with computer technologies. It uses information and communication technologies to offer medical support and services at distant locations. Telemedicine can provide a new model for interaction with the patients or other important entities such as hospitals, pharmacies, physicians and government agencies. In addition, more advanced telemedicine technologies, like telesurgery, will emerge; wherein robotic instruments will perform the surgery on the basis of the audio-visual data received by the surgeon present at a remote or a distant location. (3)

    These innovations have paved way for provision of cost-effective e-services to the world. The combination of such wireless technologies with e-health is known as mHealth. It usually includes mobile computing, medical sensor, and communications technologies.(1)  mHealth plays a crucial in enhancing communication to integrate care processes. Considering the internal processes in use at healthcare organisations, mHealth can substantially enhance the productivity of healthcare providers; thus achieving better productivity of healthcare systems. In case of external relations of healthcare organisations, mHealth can enhance transparency thereby making healthcare providers and systems accountable and ultimately empowering patients. Lastly, the greatest benefit of mHealth is to enhance the quality of life and the relevance of care. Therefore, it can help building new healthcare models, requiring a shift from inpatient to outpatient care and also enabling care delivery in rural settings and other places that lack easy access to care. (4)

    As more and more drugs get developed in laboratories than ever before, thanks to technology, it is increasing scrutiny of drug submissions as well as releasing data for compliance. Needless to say, drugs must be created as safely and securely possible, and manufacturers must provide regulators with an all-inclusive account of high-quality data that is checked in through procedures and administrative controls as an integral part of manufacturers’ processes. With exhaustive reviews of laboratory results by regulators, tools and technologies are becoming more refined to create an inclusive and complete audit trail. Again, thanks to the technological advances, the modern-day innovative tools enable manufacturers to remove any kind of interference with data, leaving them with absolute clarity while disclosing data to regulators. (5)

    Ever increasing innovations can certainly help engaging not only the smart patients, who by way of these wearables can monitor their own health, but also physicians and payers. As more and more innovations emerge, data exchange among healthcare stakeholders will become easier. This may eventually facilitate further discussion regarding opportunities as well as challenges and also possible gaps in care delivery.

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    References 

    1. Burney A, Mahmood N, Abbas Z. Information and Communication Technology in Healthcare Management Systems: Prospects for Developing. International Journal of Computer Applications (0975 – 8887) 2010; 4(2):27-32.
    2. The Benefits of Technology in Healthcare: Patient Care & Economic Boom. June, 2015. 
    3. Guthart GS, Salisbury JK. The IntuitiveTM Telesurgery System: overview and application. Proc IEEE ICRA. 2000. 
    4. Nasi G, Cucciniello M, Guerrazzi C. The Role of Mobile Technologies in Health Care Processes: The Case of Cancer Supportive Care. Eysenbach G, Tarricone R, eds. Journal of Medical Internet Research 2015; 17(2):e26.
    5. Francis G. The effect of new technologies on drug development. June, 2018. 

    Written by: Ms. Tanvi Laghate

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  • 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.

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    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. 

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  • Best Practices for Protecting Privacy While Conducting Big Data Analytics

    Best Practices for Protecting Privacy While Conducting Big Data Analytics

    The concept of “Big Data” is #trending today, which is characterized by types of data sources with huge quantities, high speed and broad diversity of information. Healthcare industries are trying to apply Big Data analytics to reform data into a workable platform in order to generate information that would help making better and faster clinical decisions, such as reduced readmissions, scaling down hospital-associated illnesses, identifying and eliminating waste, improved clinician workflow etc. Government and the private sectors are taking in Big Data to enable better, quicker and more valuable care delivery to people. (1)

    With rising discussions about Big Data, artificial intelligence, and related techniques in health care, the need for the appropriate and more importantly, ethical use of these methods is becoming increasingly relevant. (1) Privacy and confidentiality associate closely with each other. Data privacy talks about the rights of individuals to maintain control over their own health information; while confidentiality is the responsibility of entities handed over with those data to maintain privacy. (2) Concerns of data privacy and confidentiality hamper their scope, proper storage, accessibility, and propagation, particularly in case of highly sensitive or personal data. The ever expanding scope of data collection, storage and analysis (3,4), further add to the risk of data privacy infringements. (5,6) In addition, data anonymity does not ensure against individuals’ identity subsequently through the joining of data sets and re-identification, (7) data manipulation and discrimination, (8) or other inappropriate ways of data uses. (9) Therefore, protected management of patient data is necessary, since healthcare clouds link large amounts of data from disparate networks. (10)

    There are several factors of privacy and security that must be taken into consideration while using Big Data analytics for healthcare. For instance, although it has the potential to provide an understanding on the huge volumes of heterogeneous data, challenges arise with respect to potential security and privacy breaches; which, as a result, hinder the process of appropriately accessing the value held within the data. (11)

    Big Data platform must embrace multiple layers of security for data at rest and the data in flight. All communications between data sources, data consumers and the Big Data warehouse should be encrypted to provide security to the data. There are some methods that can be applied to ensure data security in Big Data analytics. A traditional method to prevent the confidential information disclosure by de-identifying, i.e. rejecting any information that can identify the patient, either by removing specific identifiers of the patient or by the second statistical method, where the patient verifies himself that enough identifiers are deleted. The traditional method can be enhanced with the help of concepts like k-anonymity, l-diversity and t-closeness. Moreover, hybrid execution model ensures confidentiality and privacy in cloud computing by utilizing public clouds only in case of non-sensitive data and computation classified as public; i.e., when the organization declares no privacy and confidentiality risk in exporting the data and performing computation on it using public clouds. While it uses private cloud in case of sensitive, private data and computation, some techniques do apply identity-based anonymization. However, due to increased complexity as well as several limitations, these models need to undergo further research and tests as they are getting more difficult to interpret and less reliable. (12)

    Patient data security and privacy are crucial in driving the healthcare transformation. With Big Data in healthcare becoming more omnipresent with cloud computing, the host companies will be more reluctant to share massive healthcare data for centralized processing. Hence, distributed processing across different clouds and pulling up on cumulative intelligence is foreseen.

    The extreme sensitivity of healthcare data makes their confidentiality and integrity crucial. Therefore, in healthcare, Big Data security is fundamental. Additionally, to provide the best care, healthcare providers must have quick, but secure, access to a patient’s medical history. Security solutions should ensure protecting analytics and securing Big Data frameworks. Laying out the right technical foundation is a precondition for successful data analysis.

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    References 

    1. Balthazar P, et al. Protecting Your Patients’ Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics.  J Am Coll Radiol 2018; 15(3 Pt B):580-586.
    2. Centers for Disease Control and Prevention. Emergency preparedness for older adults; HIPAA, privacy and confidentiality. Available at:
    3. Mittelstadt BD, et al. The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics 2016; 22:303-41.
    4. Nunan D, et al. Market research and the ethics of big data. Int J Mark Res 2013; 55:505-20.
    5. Andrejevic M. The big data divide. Int J Commun 2014;8:17.
    6. Puschmann C, Burgess J. Metaphors of big data. Int J Commun 2014;8:20.
    7. Choudhury S, et al. Big data, open science and the brain: lessons learned from genomics. Front Hum Neurosci 2014; 8:239
    8. Crawford K. The hidden biases in big data. Harvard Business Review. Available at: https://hbr.org/2013/04/the-hidden-biases-in-big-data.
    9. Tene O, et al. Big data for all: privacy and user control in the age of analytics. Nw J Tech Intell Prop 2012; 11:xxvii.
    10. Patil HK, e al. Big data security and privacy issues in healthcare. Nanthealth: Dallas, US. 
    11. [11] Rao S, et al. Security solutions for big data analytics in healthcare. Second International Conference on Advances in Computing and Communication Engineering – IEEE, 2015. 
    12. Abouelmehdi K, et al. Big data security and privacy in healthcare: A Review. Procedia Computer Science 2017; 113:73-80.

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  • Forecasting the Analytics Landscape to Anticipate Future Patient Requirements

    Forecasting the Analytics Landscape to Anticipate Future Patient Requirements

    There is heaps-full of data existing in the healthcare domain that has been generated historically, by means of record keeping, compliance & regulatory requirements, and patient care. (1) The current trend suggests faster digitization of this large amount of data, known as ‘Big Data’, that have been stored as hard copies over time. Big Data can facilitate a wide range of medical and healthcare functions, such as clinical decision support, disease surveillance, and population health management; with primary goals of providing better quality of healthcare delivery as well as reducing the costs. (2,3)

    Big data are often identified by 5V’s in terms of Volume, Velocity, Variety, Value, and Veracity. The patient data collected often amount to peta or zeta bytes in volume. The speed and rate at which data is received from the patients explains the velocity. The miscellaneous data sets classified as the structured, semi-structured and unstructured data sets like clinical reports, EHRs, and radiological images represent variety; whereas veracity is when the true and reliable data sets with accessible and genuine data are provided. The collected data are transformed to provide meaningful understanding, thus explaining their significance in 5V’s. (4)

    While multiple discussions on healthcare data are focusing excessively on probable risks and misuses of the data, there are also enormous profits from extending healthcare data usage. A whole host of use cases are available to prove the widespread value being created by data analytics, across all stakeholders of the healthcare system, including patients, healthcare professionals and providers, payers, researchers, biopharmaceuticals and medical device companies, and regulatory authorities. Patient and healthcare associations in the past, while discussing the big data applications, have focused on the potential risks linked to exploitation of personal health records (such as predictions of individual or family health risks). Progressively, however, patients are accepting the countless benefits of data analytics, while still being vigilant about possible risks linked to data misuse. (5)

    Data analytics, in conjunction with latest technologies, can help healthcare providers expand care pathways and services “beyond their walls”. For example, new measurement devices (such as wearable, ingestible or implantable sensors) can convey data that will prompt a provider to determine patient crisis. For instance, a provider can foresee and avert any complications like diabetic foot and evade amputation by monitoring vital signs in diabetic patients. In psychiatric or neurological patients, accurate supervision of a combination of indicators can provide better certainty of a crisis. Growing number of continuous monitoring services that rely remarkably on connected objects and data analytics are already altering care paradigm for chronic patients. Examples of these include Bioserenity solutions for epilepsy, Ginger.io for chronic conditions, several congestive heart failure programs being undertaken worldwide, or Diabeo for individualized insulin dosing.(5)

    As we can see, data analytics has the potential to put together predictive models and categorize patients based on the probable/future healthcare risk they might carry, in order to modify treatment protocols to their profile. These models are crucial in deciding the success of disease management programs. With increasing digital technologies, life sciences and healthcare are on the verge of a revolution. Continuous improvement in the global and quick analysis methods, initiated with genome sequencing, along with the increasing digitization of vast information, now creates substantial quantity of data. The exploitation and analysis of these big data create new prospects, while also helping address technological, scientific and medical challenges.

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    References

    1. Raghupathi W. Data Mining in Health Care. In: Kudyba S, editor. Healthcare Informatics: Improving Efficiency and Productivity. 2010. pp. 211–223.
    2. Burghard C. Big Data and Analytics Key to Accountable Care Success. 2012.
    3. Fernandes L, O’Connor M, Weaver V. J AHIMA. 2012. Big data, bigger outcomes; pp. 38–42.
    4. Sahoo PK, et al. Analyzing Healthcare Big Data With Prediction for Future Health Condition. IEEE 2017; 4:9786-9799.
    5. Unlocking the full potential of data analytics for the benefit of all. Healthcare Data Institute. November, 2015. 

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  • How India’s DISHA is Different from Global Patient Data Protection Laws?

    How India’s DISHA is Different from Global Patient Data Protection Laws?

    Currently, the data protection law in India is facing many issues due to the absence of proper legislative framework. The theft and sale of stolen data is happening across vast continents, where physical boundaries pose no restriction in today’s technologically advanced era. India, being the largest host of outsourced data processing in the world, could become the hotbed of cyber crimes; mainly owing to the lack of appropriate legislation. (1)

    To facilitate promotion/adoption of e-Health standards along with entailing privacy and security measures for electronic health data, regulation of storage, and exchange of electronic health records (EHRs); the Ministry of Health and Family Welfare, Govt. of India, is planning to enforce a ‘Digital Information Security in Healthcare Act’ (DISHA). The purpose of this act is to ensure electronic health data privacy, confidentiality, security and standardization, and to provide for establishment of ‘National Digital Health Authority’, Health Information Exchanges, and related matters. (2)

    The Centre has presented the draft of DISHA to ensure protection of health data that makes any breach punishable by up to five years imprisonment and a fine of Rs 5-lakh. This draft further states that any health data including physical, physiological and mental health condition, sexual orientation, medical records and history and biometric information are the property of the person who it pertains to. (2) This law will form the foundation for creating digital health records in India, as it will enable the digital sharing of personal health records with hospitals and clinics, and between hospitals and clinics. Reports also suggest that the National Health Policy approves the conception of a National Health Information Network, for sharing of Aadhaar linked Electronic Health Records. (3)

    What DISHA is all about? (2,3)

    As per the draft, the owners have the right to privacy, confidentiality, and security of their digital health data and the right to give or refuse consent for generation and collection of such data. Additionally, the owner of the data shall hold the rights to – i) give/refuse/withdraw consent for using this data, ii) data collection, iii) transparency, iv) rectification, v) sharing, vi) not to be refused health service if they refuse to give the consent for data use, and vii) protection.

    The required health data can be obtained by consent from the owner, thus informing him on the purpose of collection, identity of the recipients to whom the health data may be transmitted or disclosed, identity of the recipients who may have access to the data on a “need to know” basis. Also, proxy consent may be taken from a nominated representative, relative, care giver or such other person in case if an individual is incapacitated or incompetent to provide consent.

    All digital health data will be held by the clinical establishment or health information exchange on behalf of National Electronic Health Authority. The Act also lists down factors affecting data transmission as to who can transmit, how they can transmit and monitoring of data transmission. The Act further lists down the guidelines on accessing this data, with regards to who can access, how they can access, and purpose of data access by various entities. Moreover, the act also puts forth the implications of any breaches of digital data and the resulting penalties. A serious breach of this data is said to have occurred when the breach is intentional or repeated or its security not ensured as per the standards in the Act or if it is used for commercial gains.(4)

    Patient data protection laws in other countries!

    As India gears up to launch such data protection law, it may be enlightening to look at what other countries have enacted. In this context, the United States, China and the European Union have all taken drastically different directions. As stated earlier, data privacy involves getting consent from individuals before collecting their information, being transparent about why and how the information will be used, and deleting the information when it is no longer needed or when consent is withdrawn. Data protection involves taking adequate steps to protect data from accidental or malevolent leak. (5)

    The US is generally considered to have strong data privacy and protection laws (except one case in early 2017), although entangled in regulations and federal and state laws. Disclosure of health data is highly regulated at the federal level. Also, breach notification laws were pioneered in the US. The threat of legal action lawsuits compels companies to take measures to protect data and privacy. China also has multiple laws and regulations for data protection; wherein individual protections, such as requiring consent, protection of sensitive information, and limitation on use of data are provided. The latest Cybersecurity Law that rolled out on May 1, 2017 forbids people in China from using information networks to violate the privacy of others, using illegal methods to obtain personal data, and using their positions to acquire, leak, sell or share the same. In the European Union (EU), a new General Data Protection Regulation (GDPR) will be enforced starting 25th May 2018, which is expected to have a significant impact beyond the EU, because it applies to any organization that collects or processes data in the EU or from residents of the EU.5 After EU, Japan has also introduced a separate central legislation as the Act on the Protection of Personal Information (APPI) with an aim of data protection. The Act took partial effect in 2016 and has been enforceable from 30th May, 2017. Alike the EU regulation, consent of a data subject forms the essence of this legislation and has been stated as mandatory in case of transmitting data to a third party or for any use beyond communication purposes. (6)

    Amidst issues of data revelations and disclosure of personal information, India is in need of a formal legislation to uphold individual informational privacy and data protection. Internet and privacy rights supporters have demanded for such a law since long time, and the government has finally started taking steps towards this. DISHA is a result of these countless debates over data privacy issues and also, the need of the hour, i.e. protection of patient health data.  India, as a country, lags behind the world leaders when it comes to data protection. Therefore, we feel, DISHA (which is inviting public comments till 21st April) will lay the groundwork for many health exchanges while ensuring privacy and confidentiality of patient data. All we have to do now is wait and watch!

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    References 

    1. Khan MN. Does India have a Data Protection Law? Legal Service India.
    2. Government of India- Ministry of Health and Family Welfare (eHealth Section). Comments on Draft Digital Information Security in Health Care Act.(DISHA).. March 21st, 2018. 
    3. Pahwa N. Summary: Digital Information Security in Healthcare Act (DISHA) to enable electronic health records. March 29th, 2018. 
    4. Ghosh A. In draft digital health security law, 5-year jail term, Rs 5 lakh fine for data breach. March 27th, 2018.
    5. Kambampati S. What India’s data protection committee can learn from US, EU and China. October 3rd, 2017.
    6. Awasthi S. Data privacy: Where is India when it comes to legislation? August 24th, 2017.

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  • Importance of Tapping Payer’s Data to Document the Effect of a New Therapy

    Importance of Tapping Payer’s Data to Document the Effect of a New Therapy

    Traditionally, the pharmaceutical industry has always been dependent upon the ‘push’ strategy for successful market access for products. The drug approval process, involving submission of data on efficacy, safety, and tolerability to the regulatory agencies, used to be simple; which ended with the drug being marketed to the targeted physicians and dispensed by pharmacies post approval. Thus, this whole process involved a limited set of stakeholders, viz. physicians, regulatory agencies, and pharmacies. Conversely, over the years, the market access landscape has evolved primarily due to two factors: (1)

    1. Rising healthcare costs owing to an increasing prevalence of chronic diseases, growing geriatric population, and higher prices of new therapies
    2. Competitive pricing and reimbursement environment

    This has further led to the emergence of a new and diverse set of stakeholders over the years, i.e. the ‘Payer’(s), increasing the complexity of drug access to the market in general, and to patients in particular. Payer exercises the greatest degree of control over pricing and reimbursement for any new drug, and will continue to dominate the market access scenario to ensure successful market access. (2,3)

    Pharmaceutical advancements are increasingly conflicting as countries attempt to accommodate healthcare costs via different tools. New criteria for recognizing unique drugs and differences among those within the same therapeutic area or concerning the same molecule are being introduced, even though ‘price’ remains the main driver. (4) There is a surge of criticism towards the increasing prices of drugs that adds growing pressure on pharma companies and manufactures to limit future price increases, and eventually on payers to be more cost-effective in their approach to setting budgets and managing costs. (5) Global pharma operations need to keep up with the pace of these changes to approach pharma tendering as a strategy that spans pricing and commercialization.

    In order to document the effect of a new therapy in the real world, pharma companies are trying to justify prices by tapping payer’s data. Payers encourage pharma to collect post-launch evidence of product performance in the real world, thus turning it in pharma’s favor. This can help verify a price agreement or even clarify uncertainties about the clinical and/or safety outcomes outlined at registration. (6)

    The successful market access will involve collaborative team work between sales and marketing departments. The strategy itself should be well equipped to respond to market evolution and also, to accommodate all known interactions. There is no ‘one-size-fits-all’ solution. The challenges in the market will constantly vary as per the product, therapy area and the setting in which the treatment will be used.i,vi

    Payers are increasingly focusing on “real-world” outcomes to form their decisions, encouraging new policies to be formed, in order to assimilate evidence from different sources. These policies prioritize the evidence that goes beyond information collected during clinical development in randomized controlled trials (RCTs), required by regulatory authorities for marketing approval. ‘Administrative data’- that normally use retrospective or real-time patient data – are an example of the real world data sources, as they are collected primarily for reimbursement, but contain some clinical diagnosis and procedure use with detailed information on charges. Retrospective analyses (longitudinal and cross-sectional) of clinical and economic outcomes at patient, group, or population levels can be performed with the help of claims databases. Such analyses can be performed in short time and at low costs. (7)

    In conclusion, payer data from real-world such as claims data can most certainly impact the sound coverage, payment, and reimbursement decisions. It is critical that payers recognize – a) the benefits, limitations, and methodological challenges in using these data, and b) the need to carefully consider the costs and benefits of different forms of data collection in different situations.

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    References

    1. Kumar A, et al. Pharmaceutical market access in emerging markets: concepts, components, and future. Journal of Market Access & Health Policy 2014; 2:10.3402/jmahp.v2.25302.
    2. McClearn C, et al. Big pharma’s market access mission. Deloitte University Press; 2013.
    3. Arx RV, et al. Leveraging success factors for market access in the life sciences industry. Capgemini Consulting and Cegedim dendrite; 2009.
    4. Skinner JS. The costly paradox of healthcare technology. September, 2013. 
    5. Pharmaceutical pricing and market access 2017.
    6. Wechsier J. Measuring the value of prescription drugs. Pharmaceutical Executive 2017; 37(5).
    7. Garrison LP Jr. Using real-world data for coverage and payment decisions: The ISPOR Real-World Data Task Force Report. Value Health 2007; 10(5):326-225.

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  • How to Improve Healthcare Outcomes with Key Analytic Tools?

    How to Improve Healthcare Outcomes with Key Analytic Tools?

    Healthcare outcomes are defined as the changes observed and/or recorded in health status of individual or population patient/s usually due to an intervention, measures or specific healthcare investment. (1) The goal is to save the lives, shorten hospital stays and build healthier communities relying on preventative measures. (2) The fundamental steps of improving outcomes are measuring, reporting and analysing the outcomes. The efficient synthesis, organization and analysis of healthcare data offer the healthcare providers and other healthcare stakeholders with systematic and insightful treatment, measures and diagnosis. This may lead to higher patient care quality and better outcomes at lower costs.

    Healthcare industries generate a huge amount of information known as ‘big data’, driven by record keeping, compliance and regulatory requirement, potential to improve healthcare deliveries, and digitalization of historic data. (3) It include the clinical data from hospitals, clinics, pharmacies, pathological laboratories, diagnostic/imaging reports, healthcare insurances, and administrative data; individual patient data in electronic patient records (EPR) during various phases of clinical trials; pre-clinical data; hospitalization frequency data; research articles and reviews in scientific and medical journal; and information from various healthcare data resources; social media posts on different platforms; and less patient-specific information such as emergency care, news feed and healthcare magazines. (4) As per reports the data of U.S. alone may reach 1024 gigabyte soon. (3) There is need of rapidly transforming the volumes of aggregated healthcare data to value-based healthcare. 

    The analysis and assessment of huge healthcare data can be performed using advance platforms and tools with ability to handle structured, semi-structured or unstructured data. The data from random sources need to connect, match, cleanse and prepared for processing using three main steps of extract, transform and load. (4) The key platforms and tools to handle ‘big data’ are the Hadoop Distributed File System, MapReduce, PIG and PIG Latin, Hive, Jaql, Zookeeper, HBase, Cassandra, Oozie, Lucene, Avro, Mahout. (3) The analytic tools combine knowledge and data driven insights for identifying risks-factor and augmentation. These analytic tools have important applications for queries, reports, online analytical processing (OLAP) and data mining. (3) These analytic tools can search and analyse massive quantity of information from past treatments, latest published researches and healthcare databases to predict outcomes for individual patient. (5)

    Data analytic tools benefit all the components of healthcare system to improve healthcare outcomes. These components are healthcare service providers, patients, payers, stakeholders and managements. (6) Healthcare providers can develop new strategies and plan to care for patients such as reduce unnecessary hospitalizations and expenses. The patients at greatest risk of readmission can be identified and get guidance on follow ups for efficient resource utilization to save a huge amount of money spent each year on unnecessary hospitalization.

    The time gap always exists between a clinical event and the information to reach healthcare decision makers which could have bring the positive outcomes. The near real-time health surveillance can be performed using the information from social media blogs, micro-blogging on social networking sites such as Twitter and Facebook, and newspaper articles. (7) These social media networks provide information on the current locations by geo-tagged alerts. Real time analytic tools bring together the disparate information from various resources to the point of patient care, where the benefit can really be life-saving. It offers healthcare system access the most up-to date information. It realigns task based on priorities of healthcare providers, stakeholders, and insurers to improve healthcare outcomes. It addresses the gaps in care, quality, risk, utilization and regulatory requirement to support the improvements in clinical and quality outcomes; and financial performances. It provides a real-time report stating the real healthcare status of a patient and suggestions on improvement of the quality, achievement of compliance and realization of full reimbursement for their services. (8)

    It is often difficult for patients and clinician to keep the track of various healthcare organization-specific programs. The analytic tools may provide clinicians the information on a right program an eligible patient may enrol at a right time to help improve care and decrease costs. (8) The healthcare providers can assess patient-specific eligibility, gaps in care, risk scores, and historical medical information at the point of care which can be easily integrated into their existing operational model.

    The analytic tools improve healthcare outcomes by reducing the efforts and time required to handle ‘big data’ and conversion of volume to value-based information. These tools help encourage quality care to the patients benefitting payers as well as investors. The analytic tools would significantly support the advancement of medical and health science.

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    References

    1. Velentgas P., Dreyer N.A., and Wu W. A. (eds) Outcome Definition and Measurement. In ‘Developing a Protocol for Observational Comparative Effectiveness research: A User’s Guide’. Rockville,MD: Agency for Healthcare Research and Quality; AHRQ Publication No. 12(13)-EHC099, 2013.
    2. Kumar P. How real time analytics improves outcomes in healthcare. Published online on ‘IBM Cloud Blog’ dated June 19, 2017.
    3. Raghupathi W. and Raghupathi V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems 2, 3
    4. Gandomi A., and Haider M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of information Management 35, 137-144.
    5. Winters-Miner L.A. (2014) Seven ways predictive analytics can improve healthcare. Medical predictive analytics have the potential to revolutionize healthcare around the world. Published online on ‘Elsevier’s Daily stories for the science, Technology and health communities’ on Oct 06, 2014.
    6. Sun J. and Reddy C.K. (2013). Big data analytics for healthcare. Published in ‘KDD 2013 Proceedings of the 19th ACM SIAM International Conference on Knowledge Discovery and Data Mining’ held at Austin, TX, pg 1525-1525.
    7. Lee K., Agrawal A., and Choudhary A. (2013) Real-time disease surveillance using Twitter data: demonstration on flu and cancer. Published in ‘KDD 2013 proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining’, held at Chicago, Illinois, USA, pg 1474-1477.
    8. Rizzo D. The power of real-time analytics at the point of care. Published online on ‘Health IT Outcomes: Guest Column’ dated Dec 14, 2015.

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  • How to Elicit Expert Opinion to Understand Missing Health Outcomes?

    How to Elicit Expert Opinion to Understand Missing Health Outcomes?

    Missing data are a big concern in any research project and are often unavoidable in spite of investigators’ best efforts. Missing outcomes have two effects: reduced precision and power, and bias. Also, the loss of precision is inevitable, except the possible use of the available data; e.g. to be sure not to exclude from the analysis individuals who dropped out before the end of the study but who nevertheless reported intermediate values of the outcome. However, the statistician can aim to reduce bias through suitable choice of an analysis. (1)

    Randomized controlled trials (RCTs) typically have missing outcome data for some participants. Patient-reported outcomes (PROs) such as health-related quality of life (QoL) are mostly prone to missing data due to patients failing to complete follow-up questionnaires. Assumptions are often applied in case of statistical analyses for missing data to explicitly specify the values of the missing data: e.g. missing values being failures, as in smoking cessation trials. Other assumptions are inherent statements about the similarity of distributions, such as ‘last observation carried forward’. (1,2)

    In the primary trial analysis, an approach is often proposed which is valid under plausible assumptions for studies with the missing data. Instead of assuming that the data are ‘missing completely at random’ (MCAR), the primary analysis should suppose them to be ‘missing at random’ (MAR), i.e. the probability of missing data does not depend on the patient’s outcome, after conditioning on the observed variables (e.g. the patients’ baseline characteristics). However, the MAR assumption is unlikely to be used in many settings; for example, patients in relatively poor health are less likely to complete the requisite questionnaires, thus making the outcome data ‘missing not at random’ (MNAR). (2)

    The US National Research Council (NRC) report on missing data in clinical trials advocates sensitivity analyses for recognizing the data to be MNAR, in accordance with general methodological guidance for dealing with missing data and previous specific advice for intention-to-treat (ITT) analysis in RCTs. (3) On the other hand, systematic reviews show that in practice RCTs do not handle missing data appropriately. (4) Sensitivity analysis can be approached with either statistical modeling of parameters that represent outcome differences between individuals with complete versus missing data and/or exploring varying inferences with respect to the ‘sensitivity parameters’ assuming specific values. (5) The final output, i.e. results and conclusion, can then be compared over a reasonable range of values, possibly including a ‘tipping-point’ when results change. However, this approach does have a set of shortcomings. (2)

    An alternative is to allow experts to quantify their views. This is not only more intuitive and attractive for them, but it also considers a fully Bayesian approach and properly captures and reflects expert opinion (and associated uncertainty) about the missing data in the subsequent estimate of the treatment effect and its credible interval. This is particularly useful for those needing a quantitative summary of the trial, such as systematic reviewers, decision makers and health providers, as it provides a quantitative synopsis of interpretation of results by experts, given the missing data. When reviewing the study, experts will implicitly ‘fill in’ the gaps created by the missing data to arrive at their conclusions. The proposed elicitation approach, together with a Bayesian analysis, allows the study to comprehensibly quantify the impact of incorporating expert knowledge through to the estimates of treatment effectiveness.(1,2)

    Sensitivity analyses using Bayesian approach require practical tools for easier expert elicitation, and recent research focuses on elicitation approaches within group meetings. Group level elicitation has benefits for training and clarification and facilitates behavioral aggregation for achieving consensus. (6) However, because of the ‘feedback’ loop, these approaches are costly in both money and time. Thus, in many RCTs, it may not be viable to elicit opinion from a sufficient number and range of experts. Easier uptake of recommended approaches for sensitivity analysis for missing data within RCTs requires more accessible, practical tools for eliciting and synthesizing expert opinion to be developed and exemplified. (2)

    Using open source software like face-to-face or online ones to elicit beliefs from reasonably large number of experts without imposing an undue burden is one option that has been recently suggested. With this tool, the elicited views can be converted into informative priors for the sensitivity parameters in a pattern-mixture model which will allow for correlation in the elicited values across the trial arms. After this, the trial data can be re-evaluated under different MNAR assumptions to explore the robustness of the results. These methods, along with the expected level of loss to follow-up, could provide an improved estimate of the probable impact of missing data on the trial’s results. Therefore, this approach can significantly help improve trial design, so that the study results are more robust to anticipated levels of missing data.

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    References

    1. Jackson D, White IR, Leese M. How much can we learn about missing data?: an exploration of a clinical trial in psychiatry. Journal of the Royal Statistical Society Series A, (Statistics in Society). 2010; 173(3):593-612.
    2. Mason AJ, Gomes M, Grieve R, et al. Development of a practical approach to expert elicitation for randomized controlled trials with missing health outcomes: Application to the IMPROVE trial. Clinical Trials 2017; 14(4):357–367.
    3. Little RJ, D’Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med 2012; 367(14):1355–1360.
    4. Bell ML, Fiero M, Horton NJ, et al. Handling missing data in RCTs; a review of the top medical journals. BMC Med Res Methodol 2014; 14:118.
    5. Little RJA. A class of pattern-mixture models for normal incomplete data. Biometrika 1994; 81(3):471–483.
    6. O’Hagan A, Buck CE, Daneshkhah A, et al. Uncertain judgments: eliciting experts’ probabilities. 1st ed. Hoboken, NJ: John Wiley & Sons, 2006.

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