Tesi etd-09262018-162113
Link copiato negli appunti
Tipo di tesi
Perfezionamento
Autore
PIRRI, SALVATORE
URN
etd-09262018-162113
Titolo
Harnessing Big Data to track and manage medication adherence:
A novel data-driven approach.
Settore scientifico disciplinare
SECS-P/08
Corso di studi
SCIENZE ECONOMICHE E MANAGERIALI - Management
Commissione
relatore Prof. TURCHETTI, GIUSEPPE
Presidente Prof. IRALDO, FABIO
Membro Dott. DADDI, TIBERIO
Membro Prof.ssa NUTI, SABINA
Presidente Prof. IRALDO, FABIO
Membro Dott. DADDI, TIBERIO
Membro Prof.ssa NUTI, SABINA
Parole chiave
- Agent Based Model
- Big Data
- Competing Drugs.
- Digital Health
- Medication Adherence
- Network Analysis
Data inizio appello
11/12/2018;
Disponibilità
completa
Riassunto analitico
This Doctoral Thesis aims to investigate the role played by new technologies and Big Data analysis in facing and better understanding medication non-adherence phenomenon in public health, under the managerial and economic perspective.
Nearly half of patients involved in long-term treatment plan with chronic diseases are non-adherent to the medical recommendations.
Despite nowadays the diffusion of health monitoring technologies, professionals’ support and clinical treatments effectiveness are available, a robust and effective measurement for medication adherence rate and a clear understanding of the reason why patients are non-adherent to their medications plan is still needed.
This complex Public healthcare issue triggers two main issues. First, poor health outcomes for patients leading low quality of life and add more clinicians and NHS resources efforts. Second, medication non-adherence seriously affect negatively the overall National Healthcare System performance in terms of increasing costs and resources wastages.
After almost four decades of studies around the causes of medication non-adherence, scenario and procedures are quite fragmented, requiring more and new efforts in dealing with such complex and multifactorial problem worldwide recognized.
Our contribution with this Ph.D thesis is to offer a different approach, combining methods, tools and research design coming from the managerial and economic domain in order to offer a novel perspective for developing a robust strategy harnessing Big Data analytics opportunity and challenges.
The thesis combines three papers. Chapter one provides insights about the state of the art and applications for Big Data analysis in the healthcare domain, and describes the theoretical framework and the perspectives that have been adopted to inquire the research objectives of the thesis.
Chapter two analyses the literature review around the medication adherence methods adopted to measure and assess it in chronic diseases management, with a specific bibliometric analysis of the related research front.
Chapter three deals with a methodological and explorative research-field analysis, through semi-structured interviews collected from NHS professionals in a real setting in Scotland, and analyses barriers, factors, variables and IT tools faced by NHS professionals in dealing with medication adherence.
Chapter four proposes an Agent Based-Model framework that simulates the interactions between physicians and patients network in dealing with drug prescriptions. Measures and results underline how medication adherence evolves over time, and a forecasting tool useful for policy-makers in developing a more effective strategy adoption for real setting scenarios is proposed.
In chapter five, a final discussion describes a set of broad policy and managerial implications regarding non-adherence issues in the public healthcare domain and suggests future research investigations.
Nearly half of patients involved in long-term treatment plan with chronic diseases are non-adherent to the medical recommendations.
Despite nowadays the diffusion of health monitoring technologies, professionals’ support and clinical treatments effectiveness are available, a robust and effective measurement for medication adherence rate and a clear understanding of the reason why patients are non-adherent to their medications plan is still needed.
This complex Public healthcare issue triggers two main issues. First, poor health outcomes for patients leading low quality of life and add more clinicians and NHS resources efforts. Second, medication non-adherence seriously affect negatively the overall National Healthcare System performance in terms of increasing costs and resources wastages.
After almost four decades of studies around the causes of medication non-adherence, scenario and procedures are quite fragmented, requiring more and new efforts in dealing with such complex and multifactorial problem worldwide recognized.
Our contribution with this Ph.D thesis is to offer a different approach, combining methods, tools and research design coming from the managerial and economic domain in order to offer a novel perspective for developing a robust strategy harnessing Big Data analytics opportunity and challenges.
The thesis combines three papers. Chapter one provides insights about the state of the art and applications for Big Data analysis in the healthcare domain, and describes the theoretical framework and the perspectives that have been adopted to inquire the research objectives of the thesis.
Chapter two analyses the literature review around the medication adherence methods adopted to measure and assess it in chronic diseases management, with a specific bibliometric analysis of the related research front.
Chapter three deals with a methodological and explorative research-field analysis, through semi-structured interviews collected from NHS professionals in a real setting in Scotland, and analyses barriers, factors, variables and IT tools faced by NHS professionals in dealing with medication adherence.
Chapter four proposes an Agent Based-Model framework that simulates the interactions between physicians and patients network in dealing with drug prescriptions. Measures and results underline how medication adherence evolves over time, and a forecasting tool useful for policy-makers in developing a more effective strategy adoption for real setting scenarios is proposed.
In chapter five, a final discussion describes a set of broad policy and managerial implications regarding non-adherence issues in the public healthcare domain and suggests future research investigations.
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