Patient feedback data for health economic modelling: the patient´s voice integrated

DRs Nadine van Dongen

PIP health

Up to now, the reimbursement of new innovative pharmaceuticals was merely based on registration data (efficacy, safety and quality parameters). Nevertheless, increasing health care costs have become a major concern for health care decision-makers, resulting in the implementation of new cost containment measures. These measures lead to additional data requirements for new pharmaceuticals, which relates to the use of innovative medication in real daily practice. The most important new data requirements are: effectiveness, cost-effectiveness and budgetary impact. Effectiveness offers a picture of the actual value of an innovation in daily practice. Effectiveness can also be measured through measuring the level of compliance / adherence with a specific therapy. Adherence to therapies is a primary determinant of treatment success 1 .

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“Patient Intelligence refers to skills, technologies, applications, and practices used to help an organization acquire a better understanding of its position in the healthcare context.”

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Cost-effectiveness data from a state-of-the-art health economic analysis should allow reliable, reproducible and verifiable insights into the effectiveness of a drug and the possible savings that might be achieved relative to other drugs and / or treatments.

As health economic evaluation has become critical in healthcare decision-making, it is important that the methodologies used in such evaluations are continuously explored and any potential for improvement in methods or data sources is considered.

In most clinical trials, economic data are not collected alongside the study. The model, resulting from decision analysis, must correspond, as much as possible, to the real-life situation of the disease and should reflect actual treatment patterns with input values (probabilities and items of healthcare utilization) deviating as little as possible from population values 2 . Especially with regards to integrating compliance characteristics in a model, the patient’s voice is required as the patients themselves are the key determinants in the outcomes of treatment in daily life and the level of adherence with a therapy.

Patient Intelligence

Patient Intelligence refers to skills, technologies, applications, and practices used to help an organization acquire a better understanding of its position in the healthcare context 3 . Patient Intelligence may also refer to the information collected by patients. Patient Intelligence applications provide historical, current and predictive views of any given present situation regarding behaviour and intentions of persons suffering from a disorder, disease or complaint. Patient Intelligence is often aimed at the support of better decision making in the healthcare environment. Thus, a Patient Intelligence system can be called a decision support system (DSS), but the outcomes can also be used as an input for variables for health economic models 4 .

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“A patient study can be designed to measure the impact of a particular disease or condition on clinical and patient-specific outcomes…”

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An online panel database (Internet access panel) consists of a group of pre-screened respondents who have agreed to participate in surveys and / or patient feedback sessions. After completing a profiling questionnaire the respondents become “panellists.” The data collected in this questionnaire includes demographics, information on medical history, and current health status characteristics. A patient study can be designed to measure the impact of a particular disease or condition on clinical and patient-specific outcomes, and to document the outcomes associated with different treatments or settings of care in a quantitative matter. Patients can be followed prospectively and data are collected on disease severity and clinical outcomes, as well as resource use, functional status and quality of life as reported by the patient. Patient data reflect their current treatment patterns without influencing the treatments or interventions and consequently, a patient study is fully naturalistic without any intervention with real practice (e.g. no randomisation) and has a high external validity.

Patient data can yield real-life data for the comparator in the health economic model, which are based on data from daily practice. The large sample size of the online methodology may also allow the identification of any type of covariance, which could not be proven in a clinical trial because of lack of power. As a consequence, this type of study has the power for the development of statistically solid multiple regression equations with high external validity, which can be incorporated in a health economic model.

The prospective design of a patient data set allows the “a priori definition” of all economic and clinical variables, which are required for the health economic model, and which will fulfil the technical requirements of the model. Contrary, database studies are usually retrospective, which means that not all data may be collected or data may not fully correspond with the requirements of the model. Finally, most databases, especially claim databases 5 , do not contain clinical outcomes (QALYs or Quality of Life, patient satisfaction, PROs, or non-compliance), which are essential for a cost-effectiveness study. The design allows the collection of all relevant clinical outcomes, including outcomes from a patient perception, which cannot be derived from a database, whereas these outcomes are the ideal input data for a cost-effectiveness model because of its high representativeness and external validity. 6

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“The prospective design of a patient data set allows the “a priori definition” of all economic and clinical variables, which are required for the health economic model…”

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A problem associated with a database can be the lack of consensus on defining criteria for pathology, and the overlap of symptoms, as mentioned before. A patient data set can be used to exactly define the patients, which need to be included (or excluded) from the health economic model, including criteria for co-morbidity or risk factors. The databases usually contain limited sociodemographic data, but depending on the patient data source, a set can collect all relevant sociodemographic information, which is relevant for the cost-effectiveness model. 7

Conclusion

With the growing importance of modelling studies for health economic evaluations, a new area for research has been created. In order to obtain objective and reproducible results from those studies, it is important to have standardised methods of evaluation contained in accepted guidelines on methodology. This article showed that when integrating the patients’ voice in the models, a more holistic outcome will be the result, corresponding with the concept of cost-effectiveness requiring a high external validity and outcomes representing real life. The patients’ voice can be considered the optimal data source for a health economic model as it has the highest representativeness of the effectiveness of a treatment in real life. Specifically for perception sensitive factors in health economic models, like quality of life (QALYs), adherence, side-effect severity and rational discontinuation, the patients’ voice should be integrated as the patient is sole source for outcomes related to the patients’ experience with pharmaceutical therapy. Finally the use of Patient Intelligence research suits perfectly with the concept of evidence-based medicine (EBM), which means that clinical encounters should be supported by scientific conclusions based on real data as much as possible. 6

References:

1. Pateriya LP, Jha A, Munjal S. Application of information technology to overcome non-compliance of drugs. http://www.indianjournals.com/glogift2k6/glogift2k6-1-1/theme_1/Article%201.htm [Accessed April 4, 2009].

2. Weinstein MC, Fineberg HV. Clinical decision analysis. Philadelphia: WB Saunders Co., 1980.

3. Van Dongen. Are you patient intelligent? Pharmaceutical Marketing Journal 2010,1:42-3.

4. Van Dongen Nadine. Let’s be effective, let the patients talk, does patient intelligence have an effect on improvements in quality in the healthcare environment? Dovepress, 2009.

5. Nuijten MJ. The selection of data sources for use in modelling studies. Pharmacoeconomics 1998,13:305-16.

6. 1. N.v. Dongen, M.J. Nuijten, Application of PIP data in health economic models for market access, http://www.dovepress.com/application-of-pip-data-in-health-economic-models-for-market-access-peer-reviewed-article-PI, 2011.

7. Evans C. The use of consensus methods and expert panels in pharmacoeconomic studies: practical applications and method-ological shortcomings. Pharmacoeconomics 1997,12:121-.

  Clinical-trials-asia-2012

About the author:

DRs Nadine van Dongen is the founder of Patient Intelligence and the PIP Health international patient research platform. She collaborates with Diabetes UK, Women’s Health Concern, the Cure Parkinson’s Trust, National Rheumatic Arthritis Care society and many more patient advocacy groups in the United Kingdom. DRs van Dongen also acts as managing director of PIP Health, the online patient panel with access to thousands of patients in the UK participating in health related questionnaires. DRs van Dongen has published numerous peer reviewed articles on the applications of Patient Intelligence with the overall aim to improve quality of healthcare.

How can patient feedback data be improved?