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

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

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.

Precision medicine: a deep breakthrough in life sciences paradigm

Bioscience - Lost in Translation? How precision medicine closes the innovation gap

It is not so easy to translate knowledge into practice, and this is the case of biosciences into clinical applications. However, recently this trend is accelerating and precision medicine is emerging. A new book gives us the highlights to understand precisely what's going on: Bioscience - Lost in Translation? How precision medicine closes the innovation gap.

Richard Barker (the author of 2030 - The future of medicine) says:

The classic definition of diseases has been in terms of the symptoms they cause and/ or where in the body they appear. This was the best that medicine could do when external observation of the patient was the only or primary means of diagnosing disease. The  powerful new tools of molecular biology are reinterpreting disease in terms of aberrant,
defective, or unbalanced molecular mechanisms at the cellular, organ, or organism level. Molecular level diagnosis becomes a real possibility. Such an approach brings effective therapy immediately closer. Molecular diagnostics can separate diseases with similar symptoms but different underlying causes— and often suggest a different starting point for intervention.

If this is so, what should we do?

The seven changes of mindset and of practice are:
1. Advance the molecular definition of disease and the application of systems biology. We need a more decisive move from a classic definition of diseases— in terms of the symptoms they cause and/ or where in the body they appear— to a definition in terms of aberrant, defective, or unbalanced molecular mechanisms at the cellular level. And we need to marry this with a recognition that singular target- based innovation rarely works: we need a systems biology approach.

2. Partner academia and industry in more collaborative, impact- oriented research. We need to extend the ‘open innovation’ approach in which academia and companies invest together and share IP. We need to define new pre- or non- competitive spaces, especially in work on disease mechanisms and disease models. And we need to provide for new types of links and incentives to break down the barriers between these two worlds. 

3. Move decisively to a more adaptive approach to development, trial and approval design. We need to build on successful experiments in more flexible trial design, development pathways, and regulatory appraisal to a globally accepted adaptive approach. This involves collaborative design of the evidence package needed to secure approval and reimbursement, and greater teamwork through the process. 

4. Create new reward and financing vehicles for leading edge innovation. We need to move from reward systems based purely on unit sales of products, irrespective of outcome, to rewarding innovators for positive outcomes, patient by patient. We also need to design financing mechanisms that bridge between cost- effectiveness and affordability. We must be able to accommodate high- cost precision therapies that offer cures and so generate long- term returns for the system.

5. Engineer tools and systems for faster and better innovation adoption and adherence. We need to move from reliance solely on promotion to doctors and passive patient participation to a disciplined approach to establishing new pathways of care. These will be based on modern behavioural science, clinical decision support, and other digital technologies.

6. Develop an infrastructure for real- world data- driven learning. We now have the opportunity to study in large populations how lifestyle and treatment choices lead
to outcomes, learning from every patient as if in a clinical trial. New analytical tools will empower this.

7. Bring patients into the mainstream of decision- making and engage them  hole heartedly throughout the process. It is time to move from a process and mindset in which patients are regarded as passive subjects for clinical trials and recipients of products and procedures. Their input and engagement needs to be sought along the whole innovation chain: on treatment benefits, acceptable risks, optimal clinical trial design, adherence support, and outcomes.

Highly recommended.

Beyond precision medicine: high definition medicine

High-Definition Medicine

Some months ago I was posting on medicine as a data science. Now:

The foundation for a new era of data-driven medicine has been set by recent  technological advances that enable the assessment and management of human health at an unprecedented level of resolution—what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, celular reprogramming, tissue engineering, and information technologies, especially artificial intelligence.

This is what high definition medicine is about:

the dynamic assessment, management, and understanding of an individual’s health measured at (or near) its most basic units. It is the data-driven practice of medicine through the utilization of these highly detailed, longitudinal, and multi-parametric measures of the determinants of health to modify disease risk factors, detect disease processes early, drive precise and dynamically adjusted interventions, and determine preventative and therapeutic intervention efficacy from real-world outcomes

In this framework, precision medicine is only a small piece of the engine.

The article published in Cell by scholars from Scripps Translational Science Institute sheds light on the new perspectives of the practice of medicine, a milestone on the current knowledge of life sciences and its application.

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Catalunya, 1 d'octubre de 2017 · .

Genetic testing: a knotty problem

Food and Drug Administration. Optimizing FDA's regulatory oversight of next generation sequencing diagnostic tests — preliminary discussion paper

Cutting the Gordian Helix — Regulating Genomic Testing in the Era of Precision Medicine

"Scientific progress alone won't guarantee that the public reaps the full benefits of precision medicine, an achievement that will also require advancing the nation's regulatory frameworks"

This strong statement reflects a wider concern on the implementation of precision medicine or stratified medicine. I have commented before on this issue, the NEJM article of this week clarifies the last attempt by FDA to shed some light and a specific approach to disentangle the current challenges. FDA has submitted a document for comments just to start a new era of regulation in health, a "collaborative framework" for creating reliable databases of genes and genetic variants underlying disease, and provide a "safe harbor" for the interpretation of genomic tests.
This is exactly the right direction. As long as, information is a public good, genetic testing -clinical validity and utility- should be provided only by the regulator.  Professionals and citizens need to trust in precision medicine and avoid snake-oil sellers.
Having said that, today I'm more concerned than yesterday on how our government is delaying to start such effort. Today is one more day lost.

Dufy at Thyssen Museum right now

PS. Somebody should think twice about the style of health policy debates in public TV.

A bit worse before it gets better

Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease

A new mental frame was created some weeks ago when President Obama gave a speach on the creation of the initiative on Precision Medicine. To be honest, the term was in the title of a 2011 report by IOM.
In my opinion, it is a bundle: stratified medicine+big data+regulatory science+... This is the bundle of the new buzzword, and unless new details arise, nothing specially new.
Now the New Yorker speaks abouts the problems of precision medicine, and focuses on the risks. The final paragraph illustrates the issue:

For Solomon, genetics is simply a new tool with a learning curve, the same as any other. “When the electrocardiogram was first developed, about a hundred years ago, most physicians thought it was voodoo,” Solomon said. “Now, if you don’t understand it, then you shouldn’t be practicing medicine.” But Mary Norton sees that analogy as too simplistic. The pace of genetics research, the variability of test methods and results, and the aura of infallibility with which the tests are marketed, she told me, make this advance a more complicated one than the EKG. Norton believes that, as genetics becomes increasingly integrated into medical care, “over time everyone will come to have a better understanding of genetics.” But, as the demand for DNA testing increases, she says, “it will probably be a bit worse before it gets better.”

Could we avoid the initial bit worse of  "imprecision of stratified medicine"? . I'm full convinced that appropriate regulatory efforts could mitigate such impact. Unfortunately, governments are on vacation.

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

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.

Beyond health gain:the value of knowing in precision medicine

Value assessment in precision cancer medicine

Towse and Garrison provide a clear picture of the economics of using drugs with companion diagnostics (precision medicine) in the Journal of Cancer Policy. Three main issues arise:

A. Reducing or avoiding the adverse effects associated with treatment (including the medical and nonmedical costs of man-aging them).
B. Reducing or avoiding time delays in selecting the most appropriate intervention
C. Enabling a treatment effective only in a small fraction of the population to be made available or more widely available.

But there are also psychological gains (difficult to estimate) related to the value of knowing:

1. Reduction in uncertainty reflecting the idea that a companion diagnostic, by increasing the certainty of a patient’s response to a medicine—would be more valuable to individual patients and hence they (or their payer) would be willing to pay more for the combination. Furthermore, as noted above, at the population level, greater certainty could lead to greater uptake and improved compliance.
2. The value of hope is the notion that in some circumstancesindividuals become risk-seekers in the sense that they would be willing to pay more for access to a technology with a long tail indicating that some patients have a much longer survival time than current therapy, even though the average life expectancy may be no greater, or even less, than standard therapy.
3. Real option value for which the best example is that if  a treatment can extend life, this opens up possibilities for individ-uals to benefit from future advances in medicine. Hence, they(or their payer) should be willing to pay more than simply theamount they would pay for a gain in life expectancy alone, ascalculated under conventional methods, because it provides the option of benefiting from further treatments.
4. Insurance value is related to the idea that insurance tocover innovations provides peace of mind, not just by protectingagainst catastrophic financial loss but also by protecting fromcatastrophic health loss. The focus is usually only on financial protection, in the form on an Extended Cost-effectiveness Analysis. Lakdawalla et al. point out that greater value comesfrom the reassurance value of knowing of the existence of a treatment, or even of incentives to develop such a treatment.
5. Scientific spillovers arise because the benefit of scientificadvances cannot be entirely appropriated by those making them. Improving knowledge creates opportunities for additional innovation by others. For example, proving that a particular agentworks on a hypothesized pathway in a particular cancer means that the general understanding of that cancer is enhanced and thus further research can explore other pathways in the same cancer. This creates a commons problem with potential underinvestment, implying that patients may wish to reward developerswith higher prices to encourage knowledge generation.

The authors back value based pricing for drugs and companion diagnostics, just at the same time that The New York Times casts some shadows over this option.

Cantut - El Pomeró

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.

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.

 

Transforming the practice of care in the most inefficient and wasteful health system

The Smart-Medicine Solution to the Health-Care Crisis

Eric Topol provides clear insights for a wide range of life sciences issues, and some days ago he insisted once again on the need to reform US health system. Everybody is talking about financing and acces, and he focuses on organization. That's good to hear. I suggest a close look at the WSJ article. Although the scope is US, you'll find many comments that are absolutely useful for our health system (the public and specially the private one).

Our health-care system is uniquely inefficient and wasteful. The more than $3 trillion that we spend each year yields relatively poor health outcomes, compared with other developed countries that spend far less. Providing better health insurance and access can help with these problems, but real progress in containing costs and improving care will require transforming the practice of medicine itself—how we diagnose and treat patients and how patients interact with medical professionals.

And he backs a smart medicine practice:

Smart medicine offers a way out, enabling doctors to develop a precise, high-definition understanding of each person in their care. The key tools are cheaper sensors, simpler and more routine imaging, and regular use of now widely available genetic analysis. As for using all this new data, here too a revolution is under way. 

And the key integrative tool:

At the Scripps Research Institute, we are working with the support of a National Institutes of Health grant and several local partners to develop a comprehensive “health record of the future” for individual patients. It will combine all the usual medical data—from office visits, labs, scans—with data generated by personal sensors, including sleep, physical activity, weight, environment, blood pressure and other relevant medical metrics. All of it will be constantly and seamlessly updated and owned by the individual patient.

Good news (US only):

 Fortunately, serious ventures in smart medicine are well along. My colleagues and I at the Scripps Research Institute are leading the Participant Center of the NIH’s Precision Medicine Initiative, which is currently enrolling one million Americans. Volunteers in the program will be testing many of the new tools I have described here. The recently formed nonprofit Health Transformation Alliance, which includes more than 40 large companies providing health benefits to 6.5 million employees and family members, intends to address the high cost of health care by focusing on, among other things, the sophisticated use of personal data.

I have to say that his position is well grounded, it is not a fascination for technology. The true health reform starts with the practice of medicine. Completely agree.

Fotopres

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.

The value of lab testing in precision medicine

The Value of Diagnostic Testing in Personalized Medicine

Before Jevons, economists were unable to think on marginal terms. If price should be related to marginal utility, then cost pricing nowadays is outdated. However, when someone suggests value pricing, you must ask immediately about what is value for him, and maybe it is not the same than for me. A paper on lab testing and its value suggests the following:

The value of a diagnostic arises not because of its direct effect on a patient’s health but because of the information it provides on a patient’s likely response to a particular therapy. Personalized diagnostic testing reduces – though does not eliminate – the trial-and-error associated with empirical medicine, where physicians and their patients try an initial set of therapies and decide to continue or discontinue them on the basis of realized efficacy and side effects. In this manner, personalized diagnostic tests transform medical care from what economists call “experience goods,” whose quality can only be determined through consumption, to “search goods,” whose quality can be learned before  consumption

Personalized diagnostic testing offers several advantages over an empirical, trial-and-error approach to medicine. These benefits include the avoidance of side effects, potentially reduced financial costs of therapy (e.g., if a patient is identified as a likely nonresponder to an expensive therapy and the alternative is cheaper), potentially reduced opportunity costs of time – not just in terms of physician visits but also time lost on an ineffective or even harmful treatment, and improved or earlier access to effective care. Not only do patients receive value from personalized testing and treatment, but providers and health care systems benefit by avoiding ineffective, or wasteful, health care that accompanies less targeted, traditional treatment approaches. Specifically, a diagnostic test will be most valuable when the therapy being evaluated is expensive relative to alternatives, when side effects are frequent and severe (thereby making the empirical approach relatively less safe), and when delay from an alternate therapy can severely harm an individual’s health (e.g., metastatic cancer)

The concept is clear, its measurement is still uncertain.

Precision Medicine (3)

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

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

DNA methylation assays as epigenetic biomarkers

Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

A new milestone has been achieved in Medicine. Tracking epigenetic alterations is crucial to understand a disease. However, epigenetic biomarkers are needed to assess such changes. Its precision (sensitivity-specifity) is  paramount for its clinical application. Now a group of international researchers has certified its performance (partially). Have a look at this Nature article:

Genome-wide mapping and analysis of DNA methylation has become feasible for patient cohorts with thousands of samples, and epigenome-wide association studies have been conducted for numerous biomedically relevant phenotypes. To translate relevant epigenome associations into clinically useful biomarkers, it is necessary to select a manageable set of highly informative genomic regions, to target these loci with DNA methylation assays that are sufficiently fast, cheap, robust and widely available to be useful for routine clinical diagnostics, and to confirm their predictive value in large validation cohorts.

Among its conclusions I would like to highlight three of them:

(i) Absolute DNA methylation assays are the method of choice when validating DNA methylation differences in large cohorts, and they are also an excellent technology for developing epigenetic biomarkers.

(ii) Relative DNA methylation assays are not a good replacement for absolute assays. However, experiences of scientists in the contributing laboratories suggest that carefully selected, designed and validated relative assays can cost-effectively detect minimal  races of methylated DNA against an excess of unmethylated DNA.

(iii) Global DNA methylation assays suffer from noisy data and divergent results between technologies. Locus-specific assays (possibly combined with prediction) provide a more robust alternative

That's it. Very soon will see the epigenetic biomarkers in routine clinical use. And afterwards,  epigenetic drugs and treatments. Then, we'll confirm that the promise of precision medicine is a reality. The implications for medicine as a scientific discipline and clinical decision making are huge, and specifically, healthcare organizations will need to adapt to new knowledge and technologies.

PS. Neuroepigenetics: DNA methylation and memory

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 (4)

 Genomic and Precision Medicine. Foundations, Translation, and Implementation