Enciclopèdia de gestió sanitària

 Elgar Encyclopedia of Healthcare Management

 Una enciclopèdia amb aquest índex.

PART I SCENARIOS
1 Big data and artificial intelligence 2
2 Disruptive technology innovations 6
3 Genomics 8
4 Globalization 11
5 Medical tourism 13
6 Precision medicine 16
7 Robotics 19

PART II BASIC MODELS OF HEALTH SYSTEMS
8 Beveridge model 22
9 Bismarck model 24
10 Market-driven model 26

PART III EVOLUTION OF THE PHARMA AND MEDTECH INDUSTRY

11 Market access 30
12 Digital therapeutics 33
13 Biotech 36

PART IV FOUNDATIONS OF HEALTH ECONOMICS

14 Baumol’s cost disease 40
15 Disease mongering 42
16 Moral hazard in health insurance 44
17 Quasi-markets 46
18 Supplier-induced demand 48

PART V FUNDING

19 Payment mechanisms 51
20 Sources of funding 55
21 Tariff vs price 57

PART VI HEALTH POLICY PRINCIPLES

22 Equality and equity 60
23 Universalism 62
24 Well-being 64

PART VII INVESTMENT ANALYSIS

25 Business planning of healthcare services 69
26 Sources of funding for investments 71

PART VIII LEVELS OF CARE

27 Acute, sub-acute and post-acute care 77
28 Chronic care 79
29 Home care and community care 83
30 Hospital 86
31 Long term care 91
32 Prevention 93
33 Screenings 97
34 Primary healthcare 101
35 Secondary vs tertiary vs quaternary care 104

PART IX NEW PARADIGMS

36 Access to healthcare 108
37 Co-production 110
38 Demedicalization 113
39 Evidence-based medicine 115
40 From compliance to concordance 119
41 Gender medicine 121
42 Global health 123
43 Health literacy 125
44 Initiative medicine 127
45 Integrated care 130
46 Population health management 133
47 Skill mix and task shifting in healthcare 136
48 Value-based vs

PART X PLAYERS

49 Boundaryless hospital 142
50 Community and country hospital 144
51 Intermediate and transitional care settings 147
52 Primary care center 150
53 Research hospital 152
54 Teaching hospital 154

PART XI TRENDS

55 Business models 157
56 Decentralization and devolution in healthcare 159
57 Multidisciplinarity and inter- professionality 161
58 Telemedicine 164
59 Vertical and horizontal integration (hub and spoke network) 168

PART XII BEHAVIOURS:

CHALLENGES TO LEADING HEALTH ORGANIZATIONS

60 Accountability 173
61 Accountable care plan and organization 174
62 Iatocracy, professional bureaucracy and corporatization 177
63 Political arena 180
64 Professional vs managerial culture 182
65 Professionalism 184
66 Stakeholder management 186
67 Teamwork 187
68 Turf wars 189

PART XIII PRACTICES

69 Change management 193
70 Disaster management 195
71 Leadership and leadership styles 199

PART XIV ROLES

72 Case manager 203
73 Clinical engineer 205
74 Clinical leader 208
75 Controller 211
76 Family and community nurse 215
77 General practitioner 218
78 Hospitalist 220
79 Medical director 223
80 Operations manager 225
81 Pharmacist 228
82 Quality and risk manager 233

PART XV TOOLS SYSTEM AND

PROCESS: DISEASE MANAGEMENT

83 Clinical governance 237
84 Guidelines and protocols in healthcare systems 239

PART XVI INNOVATION MANAGEMENT

85 Clinical trial 243
86 Health technology assessment 246

PART XVII OPERATIONS

87 Electronic clinical records 251
88 Patient flow logistics 253
89 Patient management 256
90 Supply chain 258
91 Techniques for process and organizations improvement: lean management in healthcare 261

PART XVIII ORGANIZATION

92 Clinical service lines 264
93 Converging trends in hospital transformation 267
94 Divisionalization, clinical directorates and Troika model in healthcare 271
95 Organizational culture 273
96 Organizational design and development for healthcare organizations 276
97 Patient-centered hospital and health organization 281

PART XIX PEOPLE

98 Clinical and professional engagement 285
99 Great Place to Work® 288
100 Magnet hospital 291

PART XX PERFORMANCE

101 Balanced scorecard in healthcare organizations 294
102 Budgeting (financial vs operational) 298
103 Customer satisfaction 301
104 DRG and case mix index 303
105 Length of stay 305
106 Performance measurement and management systems 307
107 PROMs and PREMs 310
108 Strategic control 313

PART XXI PLANNING

109 Strategic planning 318
110 Strategy making 320

PART XXII PROCUREMENT

111 Centralized procurement 324
112 Innovation procurement 327
113 Managed entry agreements (MEA) 330
114 Value-based procurement 333

PART XXIII QUALITY

115 Accreditation in healthcare 337
116 Audit 340
117 Quality management 343

Precision medicine (4)

 Genomic and Precision Medicine. Foundations, Translation, and Implementation

Precision medicine (3)

The Politics of Personalised Medicine. Pharmacogenetics in the Clinic

Personalized, stratified or precision medicine: the expectations behind a concept

 Contested futures: envisioning “Personalized,” “Stratified,” and “Precision” medicine

Rather than pinpointing which of these terms is the “correct” one or delineating the “true” meaning of each, to know how we should critically approach the concepts we need an awareness of the discursive contexts in which they are mobilized. This is because the context ultimately structures the social and ethical implications that “personalization,” “stratification,” or “precision” will have for medicine and healthcare systems, and for different stakeholders. As big health data, predictive and systems-level analysis are, themselves, emergent phenomena, the terminology applied in the discursive spaces around these new biotechnologies and approaches cannot be abstracted from their context. Rather, when we apply the “personalization,” “stratification,” and “precision” terms, we invoke particular associations, connotations, “hopes” and “truths” that are part of pre-existing epistemologically and ethically loaded discourses that reflect broader and weightier struggles over what is a good future.

 

Precision medicine (2)

 Genomics and the Reimagining of Personalized Medicine

Precision medicine (2)

 Advancing Healthcare Through Personalized Medicine

Against black box medicine (2)

 Time to reality check the promises of machine learningowered precision medicine

Both machine learning and precision medicine are genuine innovations and will undoubtedly lead to some great scientific successes. However, these benefits currently fall short of the hype and expectation that has grown around them. Such a disconnect is not benign and risks overlooking rigour for rhetoric and inflating a bubble of hope that could irretrievably damage public trust when it bursts. Such mistakes and harm are inevitable if machine learning is mistakenly thought to bypass the need for genuine scientific expertise and scrutiny. There is no question that the appearance of big data and machine learning offer an exciting chance for revolution, but revolutions demand greater scrutiny, not less. This scrutiny should involve a reality check on the promises of machine learning-powered precision medicine and an enhanced focus on the core principles of good data science—trained experts in study design, data system design, and causal inference asking clear and important questions using high-quality data.

Precision medicine (2)

Discovering Precision Health: Predict, Prevent, and Cure to Advance Health and Well-Being

Introduction The Power of Precision Health 1

Chapter 1 The State of U.S. Health and Health Care Delivery 15

Chapter 2 There’s More to “Health” Than Health Care 33

Chapter 3 The Innovation and Disruption Powering Progress in Health 43

Chapter 4 Fundamental, Discovery‐Focused Research: The Foundation of Biomedical Breakthroughs 111

Chapter 5 Peering into the Future: Leveraging The Powers of Prediction to Help Prevent Illness 147

Chapter 6 Prevention as a Pathway to Health and Wellness 177

Chapter 7 Curing Disease with More Precise Medical Therapies 207

Conclusion Achieving Precision Health: The Opportunities—and Challenges—Ahead 237

Human genome 20 years later

Complicated legacies: The human genome at 20

On genome and precision medicine:

Debates about precision medicine (PM), which uses genetic information to target interventions, commonly focus on whether we can “afford” PM (17), but focusing only on affordability, not also value, risks rejecting technologies that might make health care more efficient. Affordability is a question of whether we can pay for an intervention given its impact on budgets, whereas value can be measured by the health outcomes achieved per dollar spent for an intervention. Ideally, a PM intervention both saves money and improves outcomes; however, most health care interventions produce better outcomes at higher cost, and PM is no exception. By better distinguishing affordability and value, and by considering how we can address both, we can further the agenda of achieving affordable and valuable PM.

The literature has generally not shown that PM is unaffordable or of low value; however, it has also not shown that PM is a panacea for reducing health care expenditures or always results in high-value care (17). Understanding PM affordability and value requires evidence on total costs and outcomes as well as potential cost offsets, but these data are difficult to capture because costs often occur up front while beneficial outcomes accrue over time (18). Also, PM could result in substantial downstream implications because of follow-up interventions, not only for patients but also for family members who may have inherited the same genetic condition. Emerging PM tests could be used for screening large populations and could include genome sequencing of all newborns, liquid biopsy testing to screen for cancers in routine primary care visits, and predictive testing for Alzheimer's disease in adults. These interventions may provide large benefits, but they are likely to require large up-front expenditures.

 

 

Precision medicine

 Precision Medicine for Investigators, Practitioners and Providers

Many topics under the same umbrella:

Table of Contents

Introduction

2. Role of genomics in precision medicine

3. High throughput omics in the precision medicine ecosystem

4. Infant gut microbiome

5. Paraprebiotics

6. Fecal transplantation in autoimmune disease

7. Drug pharmacomicrobiomics

8. CRISPR technology for genome editing

9. Engineering microbial living therapeutics

10. Organ on a chip

11. Multicellular in-vitro organ systems

12. The role of biobanks in biomarker development

13. Translational interest of immune profiling

14. Organoid pharmacotyping

15. Large datasets for genomic investigation

16. Modern applications of neurogenetics

17. Genomic profiling in cancer

18. Genomics in pediatrics

19. Genomics of gastric cancer

20.  Genomics of prostate cancer

21. MicroRNAs and inflammation markers in obesity

22. MiRNA sequencing for myocardial infarction screening

23. Cell free DNA in hepatocellular carcinoma

24. Non coding RNA in cancer

25. Germline variants and childhood cancer

26. Pharmacogenomics in cancer

27. Proteomic biomarkers in vireoretinal disease

28. Proteomics in respiratory diseases

29. Cardiovascular proteomics

30. Host genetics, microbiome, and inflammatory bowel disease

31. Sampling, Analyzing, and Integrating Microbiome ‘omics Data in a Translational Clinical Setting

32. Omics and microbiome in sepsis

33. Molecular and omics methods for invasive candidiasis

34. Lipid metabolism in colorectal cancer

35. Salivary volatolome in breast cancer

36. immunodiagnosis in leprosy

37. decision support systems in breast cancer

38. Electronic medical records and diabetes phenotyping

39. Clinical signature of suicide risk

40. Machine learning and cluster analysis in critical care

41. Artificial intelligence in gastroenterology

42. Algorithms for epileptic seizure prediction

43. Precision medicine in ophthalmology

44. Phenotyping COPD

45. Lifestyle medicine

46. Precision medicine for a healthier world

47. Aging and clustering of functional brain networks

48. Nutrigenetics

49. Genome editing in reproductive medicine

50. MRI guided prostate biopsy

51. Precision Nutrition

52. Theranostics in precision oncology

53. Precision medicine in daily practice

54. Imaging in precision medicine

55. Organoid for drug screening

56. Printing of personalized medication using binder jetting 3D printer

57. 3 D printing in orthopedic trauma

58. Consumer genetic testing tools in depression

59. The future of wearables

60. Tumor heterogeneity and drug development

61. Smartphone based clinical diagnosis

62. Smartphone biosensing for point of care use

63. Data security and patient protection

64. Blockchain solutions for healthcare

65. Ethical questions in gene therapy

66. Pitfalls of organ on a chip technologies

67. Regulatory issues of artificial intelligence in radiology

68. Academic industrial alliance

69. The future of precision medicine

70. Precision Medicine Glossary

71. Useful internet sites

How to set the drug market size in precision medicine

 EL SISTEMA NACIONAL DE SALUD ante la medicina de precisión

In this book you'll find my chapter with Carlos Campillo on "Los biomarcadores y la medicina de precisión", p.35

La medicina estratificada se caracteriza por la estrecha relación y dependencia entre el diagnóstico y la terapia farmacológica. La elección del punto de corte de un biomarcador (cut-off) determina la población sujeta a tratamiento y ello afecta a la rentabilidad del fármaco. La empresa farmacéutica anticipará la situación y decidirá si vale la pena situar en el mercado un medicamento estratificado o no. Además, según sea la perspectiva (pacientes o industria), las preferencias por un punto de corte diferirán y también según el tratamiento.

You'll find the details inside the chapter. 

 

Precision Medicine (3)

 The Patient Equation. The Data-Driven Future of Precision Medicine and the Business of Health Care

Precision medicine, here and now

Early Returns from the Era of Precision Medicine

Great article by David Cutler. The time for the returns of precision medicine has arrived in his opinion.

Precision medicine raises hopes for patients and fears for those who try to ride herd on health care spending. Will patients finally live longer and healthier lives? Will society be able to afford it? Surprisingly, at this point, personalized medicine has had less effect on both health and medical spending than either its strongest backers hoped or its most apprehensive actuaries feared.

Albeit,

 To date, total spending on anticancer drugs has been relatively modest. Although inflation-adjusted spending on anticancer drugs increased by $30 billion between 2011 and 2018, this is only 6% of the total increase in personal health care spending over the period. Given that administrative expenses cost an estimated 4 times the amount spent on anticancer drugs, one should be cautious about focusing excessively on the cost of precision medicine.
A better metric than total spending is cost effectiveness: do the benefits of the drugs outweigh the cost? The “drug abacus” tool developed by Memorial Sloan Kettering Cancer Center, which evaluates the cost-effectiveness of 52 anticancer drugs approved between 2001 and 2013, estimates that only a handful of new drugs are worth the cost at conventional valuations of life. If anticancer drugs were priced based on cost-effectiveness criteria, spending would fall by 30%.

This is a US based article, we need some estimates of our health system.

Hopper

A controversial view on confidence with medicine

Medical Nihilism

On Therapeutical  Nihilism and effectiveness (Ch. 11), a philosophical view:

The confidence that a medical intervention is effective ought to be low, even when presented with evidence for that intervention’s effectiveness. How low? I do not think that there can be a precise or general answer. It is enough to say: lower, often much lower, than our confidence on average now appears to be. There is surprisingly little direct study of the confidence that physicians or patients or policy-makers have regarding the effectiveness of medical interventions. However, the confidence typically
placed in medical interventions can be gauged by the resources dedicated to developing, marketing, and consuming such interventions.

What explains the disparity between the confidence placed in medical interventions and the lower confidence that I have argued we ought to have? The ingenious techniques that companies use to market their products—paying celebrities to publicly praise their products, funding consumer advocacy groups, sponsoring medical conferences,  influencing medical education, direct-to-consumer advertising—have been extensively discussed by others. The promise of scientific breakthroughs partly explains this disparity—scientists seeking support for their research programs, and companies building hype for their products, often make bold predictions about the promise of the experimental interventions they are researching, and this can sound convincing when it is put in the language of genomics, proteomics, precision medicine, personalized medicine, and evidence-based medicine. Unwarranted optimism may be based in part on a history of a few successful magic bullets, such as penicillin and insulin—magic bullet thinking gets inappropriately adopted in premature proclamations of game-changing medical interventions, which media outlets promulgate.

Medical nihilism is not the thesis that there are no effective medical interventions. Please do not confuse this. Medical nihilism is, rather, the thesis that there are fewer effective medical interventions than most people assume and that our confidence in medical interventions ought to be low, or at least much lower than is now the case.

As I said, an unconventional and controversial view. We do need measures to assess facts and knowledge, philosophy is not enough. Anyway, I recommend its reading.

All you need to know about molecular diagnostics

Molecular Diagnostics Fundamentals, Methods, and Clinical Applications

Current advances in health sciences are available at the same time that diagnostic technology and knowledge provide new tools. This book is specially relevant because it summarises all the current state of the art on molecular diagnostics. Therefore a good suggestion for those who want to practice precision medicine.

Table of contents:
I. Fundamentals of Molecular Biology: An Overview
1. Nucleic Acids and Proteins
2. Gene Expression and Epigenetics
II. Common Techniques in Molecular Biology
3. Nucleic Acid Extraction Methods
4. Resolution and Detection of Nucleic Acids
5. Analysis and Characterization of Nucleic Acids and Proteins
6. Nucleic Acid Amplification
7. Chromosomal Structure and Chromosomal Mutations
8. Gene Mutations
9. DNA Sequencing
III. Techniques in the Clinical Laboratory
10. DNA Polymorphisms and Human Identification
11. Detection and Identification of Microorganisms
12. Molecular Detection of Inherited Diseases
13. Molecular Oncology
14. DNA-Based Tissue Typing
15. Quality Assurance and Quality Control in the Molecular Laboratory
Appendices
A. Study Questions Answers
B. Answers to Case Studies
Glossary
Index

Precision Public Health

Optimizing Precision Medicine for Public Health

Precision public health (PPH) is an emerging topic of public health that complements the development of precision medicine and utilizes advances in new technologies and knowledge unlocked through big data to better target public health efforts within populations

In publically funded healthcare systems two broad priorities for decision-makers are “to do the most, for the most” (47), and to “reduce health inequity” across the population

The solution in reconciling the n of 1 with the n of many approach for precision medicine and public health respectively lies within using precision medicine  technologies to more accurately identify and define population cohorts, through increased understanding of the underlying causes and biological pathways of disease and health. That is, improved molecular understanding of disease and the underlying  biological pathways create new knowledge that unlocks opportunities for discovery and re-aggregation of patient cohorts.

This article provides some hints about the impact of precision medicine in public health. However, you'll not find the details on how to apply it in practice. We are just in the begining of this approach.

Pharm niche busters

The Information Pharms Race and Competitive Dynamics of Precision Medicine: Insights from Game Theory
Economic Dimensions of Personalized and Precision Medicine

Precision medicines inherently fragment treatment populations, generating small-population markets, creating high-priced “niche busters” rather than broadly prescribed “blockbusters”. It is plausible to expect that small markets will attract limited entry in which a small number of interdependent differentiated product oligopolists will compete, each possessing market power.

A chapter in a new book on  Precision Medicine explains the new approaches to a oligopolistic market structure where the size of the market may be determined by biomarkers with a cut-off value suggested by pharmaceutical firms themselves. The dynamics of this market is described according to game theory. Sounds fishy at least.
I already have pending chapters to read of this book. A must read for physicians and economists.

Next generation sequencing is knocking at the door (and the door is open)

Genetic testing: Opportunities to unlock value in precision medicine
Next-Generation Sequencing to Diagnose Suspected Genetic Disorders
Documento de consenso sobre la implementación de la secuenciación masiva de nueva generación en el diagnóstico genético de la predisposición hereditaria al cáncer

This week I've been reading three pieces on the same topic. First, a McKinsey insight on genetic testing, second a NEJM basic article that reviews the whole state of the issue, and third a consensus by three societies on how to implement next generation sequencing .
All of them are required reading for anyone interested in the topic. You'll notice that technology is knocking at the door and we do need to understand how to manage it. Otherwise it will enter anyway (without knocking) and then it will be more value extraction (by others) than value creation (for patients).
Unfortunately, what you'll not find in these articles is how to manage the introduction of the technology with organizational patterns, allocation and coordination of tasks and decisions. If you want some clues on this, read my previous post on Geisinger, they are applying what it seems to me the most appropriate perspective.

Sense Sal-Fins que surti el sol

Population-based genomic medicine in an integrated learning health care system

Patient-Centered Precision Health In A Learning Health Care System:Geisinger’s Genomic Medicine Experience
The Path to Routine Genomic Screening in Health Care
Medicine's future

If you want to know a latest development on the implementation of precision health, then Geisinger Health System is the place you have to go. And the Health Affairs article explains the details about the initiative and MyCode biorepository.

In 2014 the MyCode initiative began to conduct whole exome sequencing and  genotyping on collected samples, as part of a collaboration with Regeneron Pharmaceuticals and the Regeneron Genetics Center.12 Whole exome sequencing analyzes genes that code for proteins and associated gene regulatory areas—about 1–2 percent of the whole genome containing the most clinically relevant information. To date, nearly 93,000 exome sequences have been completed.

 The rapidly changing knowledge about gene-disease associations requires a process to reanalyze previously analyzed sequences and incorporate new knowledge about variants’ pathogenicity. Approximately 3.5 percent of participants have a reportable variant. As of January 2018, results had been reported to over 500 MyCode patient participants.

Interesting article, a private initiative of public interest. More info in: Science and Annals

 Josep Esquirol in Palafrugell 

Precision medicine initivatives around the world

Human genomics projects and precision medicine

Governments and research funders in developed world have decided to support precision medicine with different initiatives. Its scope and strenght it is quite diverse. It is good to know what's going on, and this is explained in an article in Nature. A data driven medicine is raising with next generation sequencing (NGS) tools:

The tremendous amount of data that NGS technologies are producing and the difficulties to manage and analyze such quantity of data require the implementation of powerful data centers for storage and analysis. Nevertheless, recent improvements in cloud computing allow managing and analyzing these huge data amounts remotely. With this goal in mind, the main internet companies have taken positions to compete in this area of NGS (data storage and analysis).
As three main examples, Google Genomics, Microsoft Genomics and Amazon Web Services (AWS) Genomics In The Cloud allow researchers to store, process, explore, and share large and complex data sets. The idea behind is to provide userfriendly tools to the researchers.

But finally it is no only for researchers, there will be one day that will be applied by clinicians. The whole article worths to be read.

Lita Cabellut. Barcelona exhibition