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Posts Tagged ‘health economics’


The Affordable Care Act: A Considered Evaluation.
Part III. Final Implementation of the Affordable Care Act and a Patient and Community Outcomes Focus

Author and Curator: Larry H Bernstein, MD, FCAP

 

UPDATED on 3/2/2018

Physicians’ Broader Vision For The Center For Medicare And Medicaid Innovation’s Future: Look Upstream

MARCH 2, 2018

https://www.healthaffairs.org/do/10.1377/hblog20180227.703418/full/

 

Introduction

This is the third discussion of a three part series on the Affordable Care Act, which is enacted and has passed review by the US Supreme Court with respect to Constitutional Legality. As a result, there is a requirement for States to implement the ACA by forming Accountable Care Organizations as a major mandate to provide an insurance safety net for the unemployed, the indigent children of unemployed or underemployed, and the highest risk population of our citizens.  The implementation of the law will take time, will need tweaking, and is already accompanied by significant reorganization of the insurance industry, which has been dominated by for-profit-organizations with a label ‘managed-care’, by the alignment of hospitals into large networks to gain leverage in negotiation of annual budget allocations, and reorganization of physicians either into very large ‘institutional providers’, or into groups of independent physicians into a ‘contract managed’ concierge group, or the persistent independent practice with assigned privileges in a department on the Medical Staff.  In any case, these arrangements are clearly matters of managing risk.  The current sequestration is an unneeded confounding factor is the matter of managing financial risk.

There are at least three issues that have surfaced:

[1] The formation of alliances of hospitals, not necessarily within one state, and the provision of care by maybe two hospitals in a community.  One interesting case is the existence of two hospitals in Erie, PA.  The Catholic Hospital has an assigned medical staff, and the other hospital is managed by University of Pittsburgh Healthcare Alliance, which is also a health insurance entity on its own.  The consequence of this arrangement is that there is no crossover of medical staff and patient choice of a physician is no longer an issue for choice.

[2] I have already mentioned where the physician is in this reorganization.  Young physicians coming into practice will choose an established group, or they might become an employee of the hospital with the ‘Part B’ payment coming through the organization’s finance (to the Medical Practice Organization), and the facilities and equipment costs taken care of by the organization.

[3] The hospital’s negotiate the insurance rates as a large network of organizations.  One risk for some members of the organization is the siphoning of cases to the strongest members of the group.  This would mean that smaller, non-metropolitan hospitals would have to refer any cases with moderate-high complexity.   That could present a problem of fairness in allocation of resources, and possibly a problem of access over large distances.

infographic The healthcare and life sciences industry is experiencing unparalleled disruption and consolidation while converging on new business models

mHealth: Managing Data on the Go

Follow the Connecting the Continuum series
By John Morrissey   Hospitals & Health News

The continuum of care requires continual communication and information sharing to tie it together, and that involves computerized equipment that clinicians and patients understand, are familiar with and will gladly use. The proliferation of cellphones, their morphing into miniature computers and the addition of wireless tablet computers have become a ready base for health-related information interchange.

The challenge for health care CEOs is to bring that potential into the particular realm of care delivery, surrounding it with reliable infrastructure and fostering policies on IT support and data security that keep a beneficial but strongly decentralizing force from getting out of corporate control, experts say.
http://www.hhnmag.com/hhnmag/images/pdf/ATTgate_july2013.pdf

A smartphone or tablet is engaging to clinicians “because it’s intuitive, it’s got the good battery life, it’s got the accessibility, fairly good speed; it brings everything to your fingertips,” says David Collins, who heads up mobile health activities with the Healthcare Information and Management Systems Society.

In contrast to interfaces for electronic record systems, which take some time to get to know and love, the intrinsic enthusiasm for mobile devices has required reining in physicians’ ambitions to use them beyond what may be practical or supportable.

An interdisciplinary committee for mobile-health policy — deciding not just device issues, but also the clinical issues of working them into health care operations — is the first step in developing a sensible rather than haphazard approach, says Collins.

Being HIPAA Compliant is a Journey

By Mike Semel

Here are a few simple things you can do to maintain a HIPAA compliant environment.

1.      HIPAA Compliant Human Resource Department

Make sure HIPAA stays on the radar of your HR staff. Be sure that HIPAA training is on the checklist for all employees. The next time a new employee is hired, ask to see the evidence that the person was trained prior to being given access to patient data. If it was done, document it as part of your internal auditing program to stay HIPAA compliant.

2.      HIPAA Compliant Employees

Audit your employees to make sure they are HIPAA compliant. Check work areas to ensure that passwords are not visible. Check the documentation for the tasks they perform. Observe them while they do their jobs. Let everyone know you are looking and conduct random HIPAA audits regularly.

3.      HIPAA Compliant Risk Analysis

Being HIPAA compliant means you will review it at least once a year. Immediately document any significant changes, like moving to a new location, relocating IT equipment to a new data center; or implementing a new EHR system. If nothing changes in a year, just make a note, and sign and date it.

4.      HIPAA Compliant Business Associates

A bigger challenge to being HIPAA compliant than your employees are your vendors. They can cause a data breach that could cost you millions of dollars. Demand evidence that they are HIPAA compliant, and their subcontractors are HIPAA compliant.

5.      Scheduling HIPAA Compliant Management

How can you remember everything needed to be HIPAA compliant?  Use your computer to schedule reminders to audit HR, your employees, and schedule reviews of the biggest threat to you staying HIPAA compliant— usually your IT company, cloud software vendor, data center, or online backup company.

ACP Concerns with Meaningful Use Program

Letter to: Sebelius, Ms. Tavenner, and Dr. Mostashari    Sep 12, 2013

On behalf of the American College of Physicians, I am writing to share our views on what has been released for Stage 2 and what we have been told to expect for Stage 3 of Meaningful Use.

ACP applauds ONC and CMS, as well as the Health IT Policy Committee and Standards Committee for their diligence and hard work in developing Stage 2 of the EHR Incentive Program. However, we are concerned that the very aggressive timeline combined with overly ambitious objectives may unnecessarily limit the success of the entire EHR Incentive program. Further, the reliance on evolving and draft standards, technologies for which integration is not yet completely tested, developing infrastructure, and upcoming regulatory requirements (i.e., ICD-10) add complexity and uncertainty to the situations faced by physicians and their teams.

As you work to transform the recommendations for Stage 3 into ambitious yet broadly achievable measures, we urge you to keep in mind the original guiding principles of the program – to position physicians and other healthcare providers to deliver excellent, patient-centered care focused on improving clinical outcomes.

While we support the goals represented by the Meaningful Use (MU) objectives, we are concerned about the appropriateness, focus and feasibility of some of the proposed measures, as well as the potential unintended consequences and additional costs to the practices of these well-intended efforts.

Return on Investment in EHRs

Meaningful Use Is Only the Beginning: Efficiency and More-Appropriate Coding Bring Savings and Increase Revenues

Today, the hope of receiving “Meaningful Use” rewards is motivating some physicians to begin using electronic health records sooner rather than later. But the government incentives will not cover all of their EHR-related costs, and there are many other reasons to get an EHR now.

Properly implemented, an EHR system with supe-rior features can:

•            Improve practice efficiency. By replacing paper records with EHRs, for example, practices can reduce record handling and access data more quickly for both clinical and billing purposes.

•            Help improve quality of care. Decision-support features can help avoid medical errors, while reporting and registry functions allow practices to track and reach out to patients who need preventive or chronic care.

•            Be a building block for a medical home. Many payers are now giving incentives to encourage physicians to create patient-centered medical homes, which require EHRs.

•            Prepare practices for accountable care: EHRs in interoperable networks are essential to accountable care organizations (ACOs).

•            Help recruit new physicians. Young doctors who trained on EHRs in residency want to work in computerized practices.

Sources Of Return On Investment (ROI)

According to experts, the incentives for Meaningful Use — up to $44,000 per provider through Medicare or nearly $64,000 through Medicaid — will cover only a portion of the long-term cost of an EHR system. Estimates of the five-year cost of EHR hardware and software range from $30,000 to $80,000 per physician, depend¬ing partly on practice size. And that doesn’t include the cost of training, interfaces, patient portals and conversions from other systems.

So a business plan for an EHR system acquisition must include sources of ROI that go beyond Meaningful Use rewards. A short list of these would include:

•            Tax write-offs (in 2011 and 2012)

•            Savings in labor and supplies

•            More accurate and complete coding, which usually results in higher revenue

•            Improved accounts receivable (A/R) manage-ment

•            Conversion of space currently used for chart storage

•            Rewards from Medicare’s Physician Quality Reporting Initiative (PQRI)

•            Pay for performance and medical home incen-tives

Except for depreciation, all of these ROI sources can be facilitated by the use of an integrated EHR and practice management (PM) system with a single database. The government’s regulations also allow physicians to show Meaningful Use by employing a combination of certified EHR modules — for example, electronic prescribing, document management, and charting systems. But if these systems are from unrelated vendors, it will be very difficult and expensive to con¬nect them with a single interface so they can work together. So, even though these modules may enable some practices to meet the Stage 1 Meaningful Use requirements, they will slow physicians down and make practices less, rather than more, efficient.

Government Incentives

To obtain financial incentives, physicians must demonstrate Meaningful Use of an EHR system certified by a government-approved certification body. In Stage 1 of Meaningful Use, a physician or other eligible professional (EP) may attest to Meaningful Use for a 90-day period in either 2011 or 2012. That attestation will entitle the EP to a payment of $18,000. Further payments fol¬low over the next four years if the EP meets the Stage 2 and 3 criteria for Meaningful Use.

EHR as a Powerful Tool in ICD-10 Conversion

The U.S. Department of Health and Human Services has mandated all health care providers begin use of ICD-10 on October 1, 2014. The conversion to the new coding set will demand incredible effort from the medical community and, if not proactively addressed, could cause major disruptions for health organizations. To complicate matters, the conversion comes at a time of other significant changes including the implementation of EHR (electronic health records). Although EHR and ICD-10 may seem like separate issues, adopting the right EHR system will help you prepare for the ICD-10 conversion. AdvancedMD EHR and integrated billing are powerful tools in the ICD-10 conversion. With over 60 years of experience, ADP is a trusted company with the knowledge and resources to give your practice the advantage in ICD-10 conversion and EHR implementation.

Getting ready for ICD-10

The conversion to ICD-10 has caused uneasiness in the health care community. The coding changes come at a time when healthcare providers are already grappling with other reforms, including the implementation of electronic health records (EHR). Recent regulations to implement ICD-10 and EHR are intended to streamline information sharing and create a more efficient national healthcare system. However, the changes can seem overwhelming for a busy private practice. Physicians are scrambling to purchase software and make upgrades before the quickly-approaching deadlines. You can’t afford to wait any longer to develop your EHR and ICD-10 implementation plans.

Although ICD-10 and EHRs may seem like separate issues, carefully designing a plan that address both your needs will save you time, money and energy. Selecting the right EHR system can aid in your conversion to ICD-10.

Today’s EHR systems are more powerful than ever. They have been designed to reflect regulatory changes to record-keeping, documentation, and coding. But not all systems are created equal— choosing an EHR system may be one of the biggest decisions you make for your practice’s financial health. EHR software should reduce the disruptions of ICD-10 conversion, not compound them.

Five things you should consider when selecting an EHR system

1. Invest in an EHR system that will be fully utilized by staff.

When you are selecting an EHR system, be sure that it will meet the specific needs of your practice. In order to reach Meaningful Use (MU) requirements and facilitate the ICD-10 conversion, your EHR system must be accessible to both clinical and administrative staff. An EHR system should meet the following standards:

•            Simple chart note creation
•            Minimal steps to access information
•            Easy-to-learn and easy-to-use interface
•            Intuitive workflow
•            Interoperability with internal and external systems

2. Choose an EHR designed to reduce ICD-10 transition challenges.

ICD-10 requires physicians and clinical staff to capture more specific patient data. With nearly nine times as many codes as ICD-9, the new coding set aims to record a higher level of medical data to use in patient care, billing, and reporting.

Additionally, EHR should aid in creating complete, detailed patient documentation. Physicians have always strove to create accurate patient charts, but the task may seem daunting with new ICD-10 codes and an expectation of increased specificity. EHR systems should provide point-and-click options to apply treatment codes and make chart notes.

3.           Ensure EHR software facilitates clinical information exchange.

When the federal government passed legislation to reform health care information technology, the reporting and exchange of patient information was a primary focus. An important consideration is how EHR technology will manage the data from other providers and health information exchanges (HIE).

Powerful EHR software makes this data an invaluable asset to patient care by intelligently organizing shared information. A private practice’s technology should present clinicians with applicable information in an easy-to-use format.

4.           Check for intelligent mapping and prompting.

An EHR system should enhance the patience experience, not complicate it. Systems that provide intelligent mapping and prompting will allow the provider to easily code and chart. Based on a patient’s history, current findings, and documentation, EHR software should suggest proposed ICD-10 codes.

Physicians can focus on engaging with the patients rather than worrying about coding proficiency or manually hunting through data screens. Intelligent mapping and prompting will reduce the time spent manually updating a patient’s chart or charge slip.

5.           Select an EHR system that will support future requirement updates.

An EHR system can be a powerful tool during the ICD-10 conversion; it can also be a hindrance. Selecting an EHR system that is capable of supporting the ICD-10 transition may be one of the most important decisions you make—but that is just a start. Be sure it will accommodate future regulatory changes.

EHR systems must be adaptable to new requirements through simple upgrades. A powerful EHR system can be updated without causing major disruption to your daily operations or to patient care. When evaluating a new system, be sure it can be modified to address future needs.

Expect more from your EHR. The EHR must provide tools that meet Meaningful Use requirements, maximize practice efficiency, and aid you in the ICD-10 conversion.

Closing Points:

•            Smoothly migrate to ICD-10 compliance with minimal disruption
•            Eliminate the costs and hassles of server-based software and hardware
•            Provide high-quality of care with access to shared health information
•            Increase proficiency and accuracy with an easy-to-learn, easy-to-use interface

Lower Health Insurance Premiums to Come at Cost of Fewer Choices

By         New York Times  Sep 22, 2013

From California to Illinois to New Hampshire, and in many states in between, insurers are driving down premiums by restricting the number of providers who will treat patients in their new health plans.WASHINGTON — Federal officials often say that health insurance will cost consumers less than expected under President Obama’s health care law. But they rarely mention one big reason: many insurers are significantly limiting the choices of doctors and hospitals available to consumers.

When insurance marketplaces open on Oct. 1, most of those shopping for coverage will be low- and moderate-income people for whom price is paramount. To hold down costs, insurers say, they have created smaller networks of doctors and hospitals than are typically found in commercial insurance. And those health care providers will, in many cases, be paid less than what they have been receiving from commercial insurers.

Some consumer advocates and health care providers are increasingly concerned. Decades of experience with Medicaid, the program for low-income people, show that having an insurance card does not guarantee access to specialists or other providers.

Consumers should be prepared for “much tighter, narrower networks” of doctors and hospitals, said Adam M. Linker, a health policy analyst at the North Carolina Justice Center, a statewide advocacy group.

“That can be positive for consumers if it holds down premiums and drives people to higher-quality providers,” Mr. Linker said. “But there is also a risk because, under some health plans, consumers can end up with astronomical costs if they go to providers outside the network
.

ED Use Could Surge Under ACA, Study Suggests

Sep 17, 2013  By Cole Petrochko,    MedPage Today

Action Points

[1] Note that this study of California registry data suggested an increase in ED visits among those insured by Medicaid from 2005-2010.

[2] Be aware that the authors speculate that the high use of the ED by Medicaid participants is due to poor access to primary care.

[3] Increases in California emergency department (ED) use were driven in large part by Medicaid patients, presaging increased burdens after the Affordable Care Act kicks in completely, researchers found.

From 2005 to 2010, the number of visits to California emergency departments rose by 13.2% from 5.4 million to 6.1 million annually, with a significant 35% increase in the number of patients insured through Medi-Cal (as Medicaid is known in California) driving this rise (P<0.001), according to Renee Hsia, MD, MSc, of the University of California San Francisco, and colleagues.

Medicaid patients also had the highest usage burden for ambulatory-care-sensitive conditions (54.76 per 1,000 patients on average) compared with those who had private insurance (10.93 per 1,000 patients) or none at all (16.6 per 1,000 patients), they wrote online in a research letter in the Journal of the American Medical Association.

According to previous research, many patients who will soon be insured under the ACA will be enrolled in Medicaid. While these people are generally healthier than current Medicaid enrollees, they may introduce a new and vast additional burden to treat undiagnosed and uncontrolled conditions.

The largest increase in visits occurred in 2009, most likely because of the “H1N1 pandemic and the influence of the economic downturn on coverage transitions and access to care,” the authors explained. Total visits per 1,000 adults living in California increased by 8.3% from 252 to 274 between 2005 and 2010.

Will healthcare reform drive up ED use?

By Alicia Caramenico
Medicaid patients use the emergency department more frequently than uninsured patients, as they still have trouble accessing primary care, according to a research letter in today’s issue of JAMA.

Researchers conducted a retrospective analysis of California ED visits by adults 19 to 64 years of age from 2005 to 2010, and found the number of visits to EDs increased by 13.2 percent to 6.1 million per year.

The largest increase in ED visit rates occurred among adult Medicaid beneficiaries, who had higher rates than both uninsured and privately insured patients.

Moreover, Medicaid patients’ high and growing ED use for ambulatory care sensitive conditions suggests the trend will continue with Medicaid expansion under healthcare reform, according to the research announcement.

Echoing those concerns, James McCarthy, M.D., of the University of Texas Health Science Center at Houston told MedPage Today the Affordable Care Act’s expansions to Medicaid “will certainly increase [ED visits] as Medicaid beneficiaries will have the most difficulty getting into primary care clinics.”

To prevent Medicaid patients from making frequent visits to the ED, hospitals could replicate efforts in Washington state that improve communication and care coordination between the ED and primary care providers, the article noted. The program in Washington educates Medicaid patients about appropriate care settings and involves case managers identifying and tracking frequent ED users, Michael Lee, M.D. of the Alpert Medical School at Brown University in Providence, R.I., told MedPage.

Hospitals should target Medicaid “super-utilizers,” using early intervention and primary care, to save money while improving the health outcomes of these complex patients, according to The Center for Medicaid and CHIP Services.

But despite concerns that high ED use by Medicaid patients stems from poor access to primary care, previous research has found most Medicaid patients go to the ED because they have to, seeking emergency or urgent care for serious medical problems, FierceHealthcare previously reported.

State Politics and the Fate of the Safety Net

K Neuhausen, M Spivey, and AL Kellermann
Sep 18, 2013       http://dx.doi.org/10.1056/NEJMp1310572             http://www.nejm.org/doi/full/10.1056/NEJMp1310572

Only 2% of acute care hospitals nationwide are safety-net facilities, but they provide 20% of uncompensated care to the uninsured. Because most are in low-income communities, they typically generate scant revenue from privately insured patients. The Medicaid Disproportionate Share Hospital (DSH) program was established to help defray their costs for uncompensated care.

Currently, Medicaid DSH disburses $11.5 billion annually to the states, which have considerable latitude in allocating these funds. Some states carefully target their DSH payments to hospitals providing large volumes of uncompensated care, but others, such as Ohio and Georgia, spread their payments broadly, transforming the program into a de facto subsidy of their hospital industry.

Because the Affordable Care Act (ACA) was expected to dramatically expand insurance coverage, safety-net hospitals were expected to need less DSH money. Therefore, to reduce the cost of expanding Medicaid, the ACA reduced Medicaid DSH funding by $18.1 billion between fiscal years 2014 and 2020. To allow time for coverage expansion to take effect, the cuts are back-loaded — starting at $500 million (4% of current national DSH spending) in 2014 but reaching $5.6 billion (49% of current spending) in 2019.

The DSH cuts are so deep in part because Congress assumed that all states would expand Medicaid, providing coverage for 17 million low-income people and sharply reducing uncompensated care. The anticipated increased revenue from Medicaid was considered sufficient to compensate hospitals for lost DSH funds. The fiscal math changed when the Supreme Court ruled that states could opt out of Medicaid expansion. Now, only 24 states and the District of Columbia plan to expand Medicaid in 2014; 22 states, including Texas and Florida, will not, and the rest are undecided. Thus, at least 6 million Americans who were expected to obtain coverage will remain uninsured. Because many states that won’t expand Medicaid currently receive large DSH payments, their safety-net hospitals will be hit hard when the DSH cuts kick in.

Even states that expand Medicaid will need some DSH support. After Massachusetts implemented its health care reform law, uncompensated-care costs at its hospitals dropped by 40% but soon climbed again. In 2011, Massachusetts hospitals required $440 million to offset their costs for uncompensated care.

Recently, the Centers for Medicare and Medicaid Services (CMS) issued a proposed rule allocating reductions in DSH payments across states for the first 2 years, on the basis of three equally weighted factors:

  1. the percentage of uninsured people in the state,
  2. how well the state targets its DSH payments to hospitals with high percentages of Medicaid inpatients,
  3. how well it targets DSH payments to hospitals with high levels of uncompensated care.

If the rule is adopted as written, states with lower percentages of uninsured citizens will receive steeper cuts, but the biggest reductions will hit states that don’t target DSH payments to hospitals providing large amounts of Medicaid and uncompensated care.

We believe the proposed rule moves DSH policy in the right direction by providing incentives to states to focus their remaining DSH funds on the hospitals that need it most. The proposed rule does not change states’ authority to use DSH funds for a broad hospital subsidy, but those that do will get less money.

States that refuse to expand Medicaid and to target DSH payments more carefully will not only forfeit billions of dollars for covering their poorest residents; they will also forgo hundreds of millions more when DSH cuts are ramped up in 2017. If politics continue to trump economic self-interest in these states, the consequences for their safety-net hospitals could be dire.

http://www.nejm.org/na101/home/literatum/publisher/mms/journals/content/nejm/0/nejm.ahead-of-print/nejmp1310572/20130918/images/small/nejmp1310572_t1.gif

If properly enforced, the proposed rule will help sustain the safety net. But if the state governments that refused to expand Medicaid also refuse to rethink their approach to allocating DSH funds, there will be little money left to sustain their safety-net hospitals when the cuts deepen in 2017. The cascade of service reductions and facility closures that this could trigger would have sweeping consequences.

Total Patient Engagement

AT Brooks, L Silverman and GR Wallen
Shared Decision Making: A Fundamental Tenet in a Conceptual Framework of Integrative Healthcare Delivery
Integrative Medicine Insights 2013:8 29–36   http://dx.doi.org/10.4137/IMI.S12783

With the increased usage of complementary and alternative medicine (CAM) in the US comes a need for evidence-based and integrated care systems which encourage open communication between patients and providers. This paper introduces a conceptual framework for integrative care delivery, with shared decision making being the “connecting force” between holistic treatment and improved health outcomes for patients.

The use of complementary and alternative medicine (CAM) is increasing. The National Center for Complementary and Alternative Medicine (NCCAM) defines CAM as “a group of diverse medical and health care … practices and products that are not generally considered part of conventional medicine” (referring to Western medicine). “Conventional” medicine is oft-referred to as allopathic, or biology-based medicine, which has emerged as the Western medical model. However, CAM is utilized by nearly half of all industrialized countries and similar or higher rates exist in many developing countries.2 These practices can be implemented together with conventional medicine, known as “complementary,” or in place of conventional medicine, known as “alternative”. Particularly in the United States, we are experiencing a shift toward combining the physiologic and technologic dimensions of curing with the spiritual dimensions of healing. The World Health Organization (WHO) recently launched a global strategy on traditional and alternative medicine, focusing on policy, safety, efficacy, and quality.4 Standardization across these dimensions has the potential to increase both access to and knowledge about CAM.

Potential barriers to CAM use and implications.

Despite developments in the field of CAM, certain barriers may inhibit its widespread adoption and integration. These potential barriers are engendered by lack of knowledge about CAM therapies, and difficulty incorporating CAM into daily routines. For treatments which require accessing a health care provider (as opposed to self-care), lack of accessibility may be an issue. Among younger individuals, the approval of family members and significant others can be important factors in individuals’ decision to use CAM.

Despite advances in technology and the power of emerging genetic and genomic discov¬eries, patients around the world are still seeking holistic, individualized care that is focused on health of both the mind and the body. Despite advances in technology and the power of emerging genetic and genomic discoveries, patients around the world are still seeking holistic, individualized care that is focused on health of both the mind and the body. Currently in the US, most patients who present to a primary care provider are scheduled into fifteen-minute visits, even though varying levels in acuity and complexity of conditions may require more intensive attention and longer visits. Expressing concern about patient needs and teaching patients how to control their symptoms are important and necessary in caring for patients in a holistic manner and require focused time and attention on the part of the health care provider. Ben-Arye and colleagues (2012) conducted a study in northern Israel and identified that patients expect that their primary care providers refer them to CAM treatments and participate in building a CAM treatment plan. Some studies suggest that making provider visits more patient-centered should be focused on “improving dialogue quality” and “efficient use of time” instead of lengthening the visits.

Patients have expressed concern about quality of care in general both in the US and internationally. Satisfaction with the care and performance delivered by our health care system is lower in the US than many other countries internationally, and health disparities within the US remain cause for concern because our current model of health care delivery is not adequate.  Experts in the field propose training more integrative health care providers to ensure that healthcare is both “high tech and high touch”.

Shared Decision-Making and CAM

The paradigm shift from “CAM” to integrative medicine reflects a need for open dialogue between patients and their providers, both conventional and CAM. Shared decision-making (SDM) between patients and providers is ethical, can preserve patient autonomy, considers patient values and preferences, and may lead to improved health outcomes. The conceptual framework introduced in this paper suggests that SDM is a vehicle that can help achieve implementation of integrative health care delivery. In a shared decision making model of care, the patient-provider relationship is interactional in nature, in that both the patient and provider are invested and actively involved in treatment decisions. Incorporating patient desires through shared decision-making (SDM) is considered to be ethical by promoting truthfulness and openness while encouraging patient autonomy. Most importantly, SDM has been associated with improved health outcomes across a range of illnesses.

The Challenge and Opportunity of ACOs: Insights from ACO Pioneers

By D Gentile, and T Samo

  1. What is an ACO?
  2. What is Clinical Integration?
  3. What is the role of Information Technology in an ACO?

How can healthcare organizations that were built on volume adapt to the arrival of a value-based reimbursement system? American providers, as well as payers, are struggling to find an answer to that critical question. When it comes to the Accountable Care Organization (ACO), the struggle generally takes two forms: either to jump in with both feet via a model such as the Medicare Pioneer ACO program, or to sit back and take a wait-and-see approach.

1.  What is an ACO?

Accountable Care Organizations are groups of physicians, hospitals and other healthcare providers in a specific geographic area who come together voluntarily to provide coordinated high quality care to their patients. The goal of coordinated care is to ensure that patients, especially the chronically ill, get the right care at the right time, while avoiding unnecessary duplication of services and preventing medical errors. When an ACO succeeds both in delivering high-quality care and spending healthcare dollars more wisely, its members share in the savings achieved for payers, whether Medicare or commercial insurers.

Medicare offers three ACO programs:

•            Medicare Shared Savings Program—a program that helps Medicare fee-for-service providers become an ACO

•            Advance Payment Initiative—a supplementary incentive program for selected participants in the Shared Savings Program

•            Pioneer ACO Model—a program designed for early adopters of coordinated care who already contract for defined populations on a risk basis

Many commercial payers have also entered into ACOs with providers, expanding on the long-standing concept of capitated reimbursement, a per-member, per-month advance payment model. In commercial ACO programs, capitated or value-based reimbursement is typically overlaid with targets for overall costs and incentive provisions for meeting cost goals and various quality metrics. Yet many commercial models are more tentative, providing arrangements such as traditional fee-for-service overlaid with shared savings and a care management fee.

2. What is Clinical Integration?

A concept that has been around for many years, clinical integration is the foundation of any ACO. Clinical integration is the means by which ACOs foster collaboration among independent physicians and hospitals to increase the quality and efficiency of patient care. Providers will need to achieve a significant level of clinical integration before they can contract with health plans, or participate in a shared savings incentives program, whether it is funded by Medicare or by commercial payers.

There are three key components of clinical integration: 1) an active, ongoing collaboration between hospitals and physicians; 2) a coordinated effort, informed by information technology, to improve the quality and efficiency of care through the use of evidence-based practices and data-driven performance improvement; and 3) an agreement with a payer that aligns the financial incentives of physicians and hospitals to accomplish these goals. In the Medicare ACO program, as well as a small but growing number of commercial programs, #3 is achieved using the shared savings approach.

3. What is the role of Information Technology in an ACO?

Successful ACOs will be those that best coordinate treatment of chronic diseases, which can, if left unchecked, balloon into expensive hospital stays. Accomplishing this requires all caregivers who treat these conditions to be in the same information loop. For most provider organizations, that means making a significant investment in information technology.

A robust IT infrastructure is required to plug the many gaps that impede the coordination of care across inpatient, outpatient and home care settings. Four basic IT components are needed: 1) a health information exchange to ensure providers across the community have access to the same patient information; 2) an interoperable Electronic Health Record (EHR) that can be accessed in multiple settings, both inpatient and outpatient, to coordinate care; 3) personal health records to help engage patients in their own health; and 4) data analytics tools to profile physicians and at-risk patients alike. Each of these technologies are now in use, but not often in a coordinated manner.

Besides these core technologies, important IT contributors to the success of an ACO include advanced utilization management functions, such as disease management, complex case management, preauthorization services, specialty referral management and other analytic tools, as well as the financial and actuarial modeling typically performed by health plans.

Four categories mirror the key constituents of an ACO: physicians, payers, hospitals and health systems and patients. A fifth category describes an ACO’s organizational imperative – helping these groups to work together by building a shared identity.

Physician:
•            Physician leadership is critical
•            Local governance advances shared goals
•            Equip physicians with infrastructure to succeed
•            Work to engage independent physicians
•            Use both local and global incentives
•            Educate and train on a schedule
•            Monitor physician performance

The ACO flips the traditional adversarial relationship between hospitals and physicians on its head. To be successful, an ACO requires shared, consensual leadership between hospitals and physicians, who come to the table as fully equal partners in the new organization.

Use of Clinical Analytics in the World of Meaningful Use

Feb 2011  Sponsored by Anvita Health

In June 2010, HIMSS Analytics released a white paper that addressed the use of clinical analytics in the marketplace. At that time, most of the respondents participating in this research indicated that they were actively engaged in collecting and/or leveraging both clinical and claims data to enhance patient care cost, safety, efficiency and reducing healthcare costs. It was noted that none of the applications in the EMR suite had reached market saturation. And, while utilization of each of these applications has increased in the past year, that is still the case.

It is this growth in EMR adoption which is one of the principal drivers of the increased use of clinical analytics, since it is the patient data captured by these applications that is the primary source of the information that healthcare organizations analyze using clinical analytics tools. Spurred by Title XIII of the American Recovery and Reinvestment Act (ARRA) adoption of these technologies is expected to continue to accelerate in the future. In July 2010, the Centers for Medicare and Medicaid Services (CMS) published the final rules on the Electronic Health Record Incentive Program. According to the Federal Register, “The HITECH Act statutorily requires the use of health information technology in improving the quality of care, reducing medical errors, reducing health disparities, increasing prevention and improving the continuity of care among health settings”. In order to meet the goals of this statement and receive incentive payments, CMS identified a core set of 14 meaningful use objectives on which eligible hospitals need to focus to qualify for incentive funds provided through the new CMS Medicare and Medicaid incentive program. Additionally, eligible hospitals must achieve five of 10 menu set objectives to qualify for incentive funds.

In addition to a focus on meaningful use measures, the industry’s shift to the use of ICD-10 (International Statistical Classification of Diseases and Related Health Problems-10th revision), mandated for the coding of all inpatient and outpatient claims beginning in October 20132, will also impact the use of clinical analytics.

1 HIMSS http://www.himss.org/content/files/MU Final Rule.pdf 
2 Centers for Medicare & Medicaid Services https://www.cms.gov/ICD10/

The increased granularity from ICD-10, combined with the increased electronic capture of clinical data will yield volumes of new data for which healthcare organizations will have the opportunity to translate into information that can be used to improve the delivery of healthcare in the United States. However, for this to be successful, healthcare organizations will need both the tools to review and analyze data and an environment, such as a data warehouse in which to store and stage the data for efficient analysis.

Drivers for Using Clinical Analytics

In the research conducted in 2010, two key drivers for using clinical analytics to translate data into information were identified. These were achieving a high quality of care and patient safety and increasing awareness about the costs associated with the provision of care. These two factors continue to be the principal drivers in the market, as respondents indicated that they are continuing to try to provide a high level of care to individuals in their service area, while carefully monitoring and managing costs.

One way in which organizations are framing the quality of care issues is within the context of meaningful use, which has become a powerful industry driver. Because of the financial carrot of incentives when meaningful use criteria are met, many healthcare organizations (HCOs) are evaluating how they are capturing and analyzing data. All of the respondents noted that they are carefully analyzing the data that is being generated during the care delivery process and mapping that data against the process measures, such as capturing flow sheet data and changes in vital signs that have been identified in the meaningful use criteria or entering orders using computerized practitioner order entry (CPOE). And, because organizations will be required to report on multiple measures to achieve the meaningful use incentives, they are driven to find ways to be able to capture and report successfully on all measures rather than focusing on only a handful of measures.

Cost control also continues to be a key driver for these organizations, and has become an area of heightened concern over the course of the past year. Healthcare organizations are under pressure to meet increased demands for services, while at the same time containing costs. Additionally, as HCOs shift to an environment in which Patient Centered Medical Homes (PCMH) and Accountable Care Organizations (ACOs) are being touted as key solutions for the future, HCOs are looking for ways to limit their financial risk and provide care in a smarter, more efficient and more cost-effective fashion. As such, both payer and provider respondents in this research suggested that they look at data that had the potential to allow them to improve the financial bottom line at their organizations.

Current Use of Clinical Analytics

Most of the respondents participating in the June 2010 research reported that they are collecting and/or leveraging clinical and/or claims data to enhance patient care cost, safety and efficiency. The respondents from the current research cited similar approaches. To ensure that they are able to understand trends emerging within their patient population, respondents from the HCOs represented in this study reported analyzing data from wide variety of departments within their organizations. Some of the data sources identified by the respondents from provider organizations included OR, other procedural suites and the emergency department (ED). They also noted that medication, laboratory, billing and claims data were also analyzed. A number of respondents are also looking at data captured in ambulatory environments. The payer respondents in this research are also analyzing data from a wide variety of sources, including laboratory data, pharmacy data and claims (i.e. UB92) data.

Data Sharing

In addition to patient data that is captured at the HCO that is providing care, respondents reported sharing data with other organizations such as Midas, United Hospital Consortium (UHC), Premier and Health Plan Employer Data and Information Set (HEDIS). In conjunction with their own data, these external data sources allow HCOs to create a series of benchmarking reports that help them identify and analyze variances on their performance compared to other organizations of similar size and composition on key metrics such as length of stay, case costs and outcomes measures. Respondents from payer organizations are also relying on external metrics such as HEDIS and CAHPS (Consumer Assessment of Healthplan Providers and Systems) to direct their analysis.

A 3-Year M.D. — Accelerating Careers, Diminishing Debt

SB Abramson, D Jacob, M Rosenfeld, et al.
It’s been more than 100 years since Abraham Flexner proposed the current model for medical education in North America: 2 years of basic science instruction followed by 2 years of clinical experience.1 Over the past several decades, major changes have caused the medical community to reconsider current educational models. These changes include increasing education costs, shifts in health care needs, the demographics of the applicant pool, and many scientific, pharmacologic, and technological advances resulting in increased specialization of physicians.

Oversight of U.S. medical education is compartmentalized, with standards independently set for undergraduate and graduate accreditation by the Liaison Committee on Medical Education (LCME) and the Accreditation Council for Graduate Medical Education (ACGME), respectively. This system results in rigid, time-based, non–learner-centered training. Recognizing this limitation, the Carnegie Foundation recently recommended that education should “provide options for individualizing the learning process for students and residents, such as offering the possibility of fast tracking within and across levels.”

In the past 30 years, the required training period after medical school has increased substantially,2 but the time spent in medical school has not been shortened. The average age of physicians entering practice has therefore increased. Since 1975, the percentage of physicians who are younger than 35 years of age has decreased from 28% to 15% (see graph), as the prolongation of specialty training has delayed entry into the workforce, reducing the productive years of clinicians and physician scientists. Compounding the effect of the increased duration of training is the growing number of entering medical students who have taken “gap” years between college and medical school. National data indicate that the average age of first-year medical students is 24. At the New York University School of Medicine (NYUSOM), 55% of this year’s entering medical students have taken 1 or more gap years.

http://www.nejm.org/na101/home/literatum/publisher/mms/journals/content/nejm/2013/nejm_2013.369.issue-12/nejmp1304681/20130918/images/small/nejmp1304681_f1.gif

Percentage of Physicians in the United States Who Are Younger Than 35 Years of Age, 1975–2011.

The Challenge and Opportunity of ACOs: Insights from ACO Pioneers

Djen Linji    http://bit.ly/acochallenges
How can healthcare organizations that were built on volume adapt to the arrival of a value-based reimbursement system? American providers, as well as payers, are struggling to find an answer to that critical question. When it comes to the Accountable Care Organization (ACO), the struggle generally takes two forms: either to jump in with both feet via a model such as the Medicare Pioneer ACO program, or to sit back and take a wait-and-see approach.

Related Articles in Pharmaceutical Intelligence.com

The Affordable Care Act: A Considered Evaluation.
Part I.  The legislative act (ACA) and the model for implementation (Insurance Gateways).

Larry H. Bernstein, and Aviva Lev-Ari

https://pharmaceuticalintelligence.com/2013/09/13/the-affordable-care-act-a-considered-evaluation-the-legislative-act-aca-and-the-model-for-implementation-insurance-gateways/

The Affordable Care Act: A Considered Evaluation.
Part II: The Implementation of the ACA, Impact on Physicians and Patients, and the Dis-Ease of the Accountable Care Organizations.

Larry H. Bernstein, and Aviva Lev-Ari

https://pharmaceuticalintelligence.com/2013/09/13/the-affordable-care-act-a-considered-evaluation-the-implementation-of-the-aca-impact-on-physicians-and-patients-and-the-dis-ease-of-the-accountable-care-organizations/

Innovators-Prescription-New-Wave-of-Disruptive-Models-in-Healthcare

hhs_medicare_docs participating in and billing Medicare

healthprices time price of HC over 50 years

NHEbyDCforHS1 NHE annual growth rate of 4%

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Larry H Bernstein, MD
Leaders in Pharmaceutical Intelligence

 

I call attention to an interesting article that just came out.   The estimate of improved costsavings in healthcare and diagnostic accuracy is extimated to be substantial.   I have written about the unused potential that we have not yet seen.  In short, there is justification in substantial investment in resources to this, as has been proposed as a critical goal.  Does this mean a reduction in staffing?  I wouldn’t look at it that way.  The two huge benefits that would accrue are:

 

  1. workflow efficiency, reducing stress and facilitating decision-making.
  2. scientifically, primary knowledge-based  decision-support by well developed algotithms that have been at the heart of computational-genomics.

 

 

 

Can computers save health care? IU research shows lower costs, better outcomes

Cost per unit of outcome was $189, versus $497 for treatment as usual

 Last modified: Monday, February 11, 2013

 

BLOOMINGTON, Ind. — New research from Indiana University has found that machine learning — the same computer science discipline that helped create voice recognition systems, self-driving cars and credit card fraud detection systems — can drastically improve both the cost and quality of health care in the United States.

 

 

 Physicians using an artificial intelligence framework that predicts future outcomes would have better patient outcomes while significantly lowering health care costs.

 

 

Using an artificial intelligence framework combining Markov Decision Processes and Dynamic Decision Networks, IU School of Informatics and Computing researchers Casey Bennett and Kris Hauser show how simulation modeling that understands and predicts the outcomes of treatment could

 

  • reduce health care costs by over 50 percent while also
  • improving patient outcomes by nearly 50 percent.

 

The work by Hauser, an assistant professor of computer science, and Ph.D. student Bennett improves upon their earlier work that

 

  • showed how machine learning could determine the best treatment at a single point in time for an individual patient.

 

By using a new framework that employs sequential decision-making, the previous single-decision research

 

  • can be expanded into models that simulate numerous alternative treatment paths out into the future;
  • maintain beliefs about patient health status over time even when measurements are unavailable or uncertain; and
  • continually plan/re-plan as new information becomes available.

In other words, it can “think like a doctor.”  (Perhaps better because of the limitation in the amount of information a bright, competent physician can handle without error!)

 

“The Markov Decision Processes and Dynamic Decision Networks enable the system to deliberate about the future, considering all the different possible sequences of actions and effects in advance, even in cases where we are unsure of the effects,” Bennett said.  Moreover, the approach is non-disease-specific — it could work for any diagnosis or disorder, simply by plugging in the relevant information.  (This actually raises the question of what the information input is, and the cost of inputting.)

 

The new work addresses three vexing issues related to health care in the U.S.:

 

  1. rising costs expected to reach 30 percent of the gross domestic product by 2050;
  2. a quality of care where patients receive correct diagnosis and treatment less than half the time on a first visit;
  3. and a lag time of 13 to 17 years between research and practice in clinical care.

  Framework for Simulating Clinical Decision-Making

 

“We’re using modern computational approaches to learn from clinical data and develop complex plans through the simulation of numerous, alternative sequential decision paths,” Bennett said. “The framework here easily out-performs the current treatment-as-usual, case-rate/fee-for-service models of health care.”  (see the above)

 

Bennett is also a data architect and research fellow with Centerstone Research Institute, the research arm of Centerstone, the nation’s largest not-for-profit provider of community-based behavioral health care. The two researchers had access to clinical data, demographics and other information on over 6,700 patients who had major clinical depression diagnoses, of which about 65 to 70 percent had co-occurring chronic physical disorders like diabetes, hypertension and cardiovascular disease.  Using 500 randomly selected patients from that group for simulations, the two

 

  • compared actual doctor performance and patient outcomes against
  • sequential decision-making models

using real patient data.

They found great disparity in the cost per unit of outcome change when the artificial intelligence model’s

 

  1. cost of $189 was compared to the treatment-as-usual cost of $497.
  2. the AI approach obtained a 30 to 35 percent increase in patient outcomes
Bennett said that “tweaking certain model parameters could enhance the outcome advantage to about 50 percent more improvement at about half the cost.”

 

While most medical decisions are based on case-by-case, experience-based approaches, there is a growing body of evidence that complex treatment decisions might be effectively improved by AI modeling.  Hauser said “Modeling lets us see more possibilities out to a further point –  because they just don’t have all of that information available to them.”  (Even then, the other issue is the processing of the information presented.)

 

 

Using the growing availability of electronic health records, health information exchanges, large public biomedical databases and machine learning algorithms, the researchers believe the approach could serve as the basis for personalized treatment through integration of diverse, large-scale data passed along to clinicians at the time of decision-making for each patient. Centerstone alone, Bennett noted, has access to health information on over 1 million patients each year. “Even with the development of new AI techniques that can approximate or even surpass human decision-making performance, we believe that the most effective long-term path could be combining artificial intelligence with human clinicians,” Bennett said. “Let humans do what they do well, and let machines do what they do well. In the end, we may maximize the potential of both.”

 

 

Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach” was published recently in Artificial Intelligence in Medicine. The research was funded by the Ayers Foundation, the Joe C. Davis Foundation and Indiana University.

 

For more information or to speak with Hauser or Bennett, please contact Steve Chaplin, IU Communications, at 812-856-1896 or stjchap@iu.edu.

 

 

IBM Watson Finally Graduates Medical School

 

It’s been more than a year since IBM’s Watson computer appeared on Jeopardy and defeated several of the game show’s top champions. Since then the supercomputer has been furiously “studying” the healthcare literature in the hope that it can beat a far more hideous enemy: the 400-plus biomolecular puzzles we collectively refer to as cancer.

 

 

 

Anomaly Based Interpretation of Clinical and Laboratory Syndromic Classes

Larry H Bernstein, MD, Gil David, PhD, Ronald R Coifman, PhD.  Program in Applied Mathematics, Yale University, Triplex Medical Science.

 

 Statement of Inferential  Second Opinion

 Realtime Clinical Expert Support and Validation System

Gil David and Larry Bernstein have developed, in consultation with Prof. Ronald Coifman, in the Yale University Applied Mathematics Program, a software system that is the equivalent of an intelligent Electronic Health Records Dashboard that provides
  • empirical medical reference and suggests quantitative diagnostics options.

Background

The current design of the Electronic Medical Record (EMR) is a linear presentation of portions of the record by
  • services, by
  • diagnostic method, and by
  • date, to cite examples.

This allows perusal through a graphical user interface (GUI) that partitions the information or necessary reports in a workstation entered by keying to icons.  This requires that the medical practitioner finds

  • the history,
  • medications,
  • laboratory reports,
  • cardiac imaging and EKGs, and
  • radiology
in different workspaces.  The introduction of a DASHBOARD has allowed a presentation of
  • drug reactions,
  • allergies,
  • primary and secondary diagnoses, and
  • critical information about any patient the care giver needing access to the record.
 The advantage of this innovation is obvious.  The startup problem is what information is presented and how it is displayed, which is a source of variability and a key to its success.

Proposal

We are proposing an innovation that supercedes the main design elements of a DASHBOARD and
  • utilizes the conjoined syndromic features of the disparate data elements.
So the important determinant of the success of this endeavor is that it facilitates both
  1. the workflow and
  2. the decision-making process
  • with a reduction of medical error.
 This has become extremely important and urgent in the 10 years since the publication “To Err is Human”, and the newly published finding that reduction of error is as elusive as reduction in cost.  Whether they are counterproductive when approached in the wrong way may be subject to debate.
We initially confine our approach to laboratory data because it is collected on all patients, ambulatory and acutely ill, because the data is objective and quality controlled, and because
  • laboratory combinatorial patterns emerge with the development and course of disease.  Continuing work is in progress in extending the capabilities with model data-sets, and sufficient data.
It is true that the extraction of data from disparate sources will, in the long run, further improve this process.  For instance, the finding of both ST depression on EKG coincident with an increase of a cardiac biomarker (troponin) above a level determined by a receiver operator curve (ROC) analysis, particularly in the absence of substantially reduced renal function.
The conversion of hematology based data into useful clinical information requires the establishment of problem-solving constructs based on the measured data.  Traditionally this has been accomplished by an intuitive interpretation of the data by the individual clinician.  Through the application of geometric clustering analysis the data may interpreted in a more sophisticated fashion in order to create a more reliable and valid knowledge-based opinion.
The most commonly ordered test used for managing patients worldwide is the hemogram that often incorporates the review of a peripheral smear.  While the hemogram has undergone progressive modification of the measured features over time the subsequent expansion of the panel of tests has provided a window into the cellular changes in the production, release or suppression of the formed elements from the blood-forming organ to the circulation.  In the hemogram one can view data reflecting the characteristics of a broad spectrum of medical conditions.
Progressive modification of the measured features of the hemogram has delineated characteristics expressed as measurements of
  • size,
  • density, and
  • concentration,
resulting in more than a dozen composite variables, including the
  1. mean corpuscular volume (MCV),
  2. mean corpuscular hemoglobin concentration (MCHC),
  3. mean corpuscular hemoglobin (MCH),
  4. total white cell count (WBC),
  5. total lymphocyte count,
  6. neutrophil count (mature granulocyte count and bands),
  7. monocytes,
  8. eosinophils,
  9. basophils,
  10. platelet count, and
  11. mean platelet volume (MPV),
  12. blasts,
  13. reticulocytes and
  14. platelet clumps,
  15. perhaps the percent immature neutrophils (not bands)
  16. as well as other features of classification.
The use of such variables combined with additional clinical information including serum chemistry analysis (such as the Comprehensive Metabolic Profile (CMP)) in conjunction with the clinical history and examination complete the traditional problem-solving construct. The intuitive approach applied by the individual clinician is limited, however,
  1. by experience,
  2. memory and
  3. cognition.
The application of rules-based, automated problem solving may provide a more reliable and valid approach to the classification and interpretation of the data used to determine a knowledge-based clinical opinion.
The classification of the available hematologic data in order to formulate a predictive model may be accomplished through mathematical models that offer a more reliable and valid approach than the intuitive knowledge-based opinion of the individual clinician.  The exponential growth of knowledge since the mapping of the human genome has been enabled by parallel advances in applied mathematics that have not been a part of traditional clinical problem solving.  In a univariate universe the individual has significant control in visualizing data because unlike data may be identified by methods that rely on distributional assumptions.  As the complexity of statistical models has increased, involving the use of several predictors for different clinical classifications, the dependencies have become less clear to the individual.  The powerful statistical tools now available are not dependent on distributional assumptions, and allow classification and prediction in a way that cannot be achieved by the individual clinician intuitively. Contemporary statistical modeling has a primary goal of finding an underlying structure in studied data sets.
In the diagnosis of anemia the variables MCV,MCHC and MCH classify the disease process  into microcytic, normocytic and macrocytic categories.  Further consideration of
proliferation of marrow precursors,
  • the domination of a cell line, and
  • features of suppression of hematopoiesis

provide a two dimensional model.  Several other possible dimensions are created by consideration of

  • the maturity of the circulating cells.
The development of an evidence-based inference engine that can substantially interpret the data at hand and convert it in real time to a “knowledge-based opinion” may improve clinical problem solving by incorporating multiple complex clinical features as well as duration of onset into the model.
An example of a difficult area for clinical problem solving is found in the diagnosis of SIRS and associated sepsis.  SIRS (and associated sepsis) is a costly diagnosis in hospitalized patients.   Failure to diagnose sepsis in a timely manner creates a potential financial and safety hazard.  The early diagnosis of SIRS/sepsis is made by the application of defined criteria (temperature, heart rate, respiratory rate and WBC count) by the clinician.   The application of those clinical criteria, however, defines the condition after it has developed and has not provided a reliable method for the early diagnosis of SIRS.  The early diagnosis of SIRS may possibly be enhanced by the measurement of proteomic biomarkers, including transthyretin, C-reactive protein and procalcitonin.  Immature granulocyte (IG) measurement has been proposed as a more readily available indicator of the presence of
  • granulocyte precursors (left shift).
The use of such markers, obtained by automated systems in conjunction with innovative statistical modeling, may provide a mechanism to enhance workflow and decision making.
An accurate classification based on the multiplicity of available data can be provided by an innovative system that utilizes  the conjoined syndromic features of disparate data elements.  Such a system has the potential to facilitate both the workflow and the decision-making process with an anticipated reduction of medical error.
This study is only an extension of our approach to repairing a longstanding problem in the construction of the many-sided electronic medical record (EMR).  On the one hand, past history combined with the development of Diagnosis Related Groups (DRGs) in the 1980s have driven the technology development in the direction of “billing capture”, which has been a focus of epidemiological studies in health services research using data mining.

In a classic study carried out at Bell Laboratories, Didner found that information technologies reflect the view of the creators, not the users, and Front-to-Back Design (R Didner) is needed.  He expresses the view:

“Pre-printed forms are much more amenable to computer-based storage and processing, and would improve the efficiency with which the insurance carriers process this information.  However, pre-printed forms can have a rather severe downside. By providing pre-printed forms that a physician completes
to record the diagnostic questions asked,
  • as well as tests, and results,
  • the sequence of tests and questions,
might be altered from that which a physician would ordinarily follow.  This sequence change could improve outcomes in rare cases, but it is more likely to worsen outcomes. “

Decision Making in the Clinical Setting.   Robert S. Didner

 A well-documented problem in the medical profession is the level of effort dedicated to administration and paperwork necessitated by health insurers, HMOs and other parties (ref).  This effort is currently estimated at 50% of a typical physician’s practice activity.  Obviously this contributes to the high cost of medical care.  A key element in the cost/effort composition is the retranscription of clinical data after the point at which it is collected.  Costs would be reduced, and accuracy improved, if the clinical data could be captured directly at the point it is generated, in a form suitable for transmission to insurers, or machine transformable into other formats.  Such data capture, could also be used to improve the form and structure of how this information is viewed by physicians, and form a basis of a more comprehensive database linking clinical protocols to outcomes, that could improve the knowledge of this relationship, hence clinical outcomes.
 How we frame our expectations is so important that
  • it determines the data we collect to examine the process.
In the absence of data to support an assumed benefit, there is no proof of validity at whatever cost.   This has meaning for
  • hospital operations, for
  • nonhospital laboratory operations, for
  • companies in the diagnostic business, and
  • for planning of health systems.
In 1983, a vision for creating the EMR was introduced by Lawrence Weed and others.  This is expressed by McGowan and Winstead-Fry.
J J McGowan and P Winstead-Fry. Problem Knowledge Couplers: reengineering evidence-based medicine through interdisciplinary development, decision support, and research.
Bull Med Libr Assoc. 1999 October; 87(4): 462–470.   PMCID: PMC226622    Copyright notice

 

Example of Markov Decision Process (MDP) trans...

Example of Markov Decision Process (MDP) transition automaton (Photo credit: Wikipedia)

Control loop of a Markov Decision Process

Control loop of a Markov Decision Process (Photo credit: Wikipedia)

 

English: IBM's Watson computer, Yorktown Heigh...

English: IBM’s Watson computer, Yorktown Heights, NY (Photo credit: Wikipedia)

English: Increasing decision stakes and system...

English: Increasing decision stakes and systems uncertainties entail new problem solving strategies. Image based on a diagram by Funtowicz, S. and Ravetz, J. (1993) “Science for the post-normal age” Futures 25:735–55 (http://dx.doi.org/10.1016/0016-3287(93)90022-L). (Photo credit: Wikipedia)

 

 

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The Incentive for “Imaging based cancer patient’ management”


Author and Curator: Dror Nir, PhD

It is generally agreed by radiologists and oncologists that in order to provide a comprehensive work-flow that complies with the principles of personalized medicine, future cancer patients’ management will heavily rely on “smart imaging” applications. These could be accompanied by highly sensitive and specific bio-markers, which are expected to be delivered by pharmaceutical companies in the upcoming decade. In the context of this post, smart imaging refers to imaging systems that are enhanced with tissue characterization and computerized image interpretation applications. It is expected that such systems will enable gathering of comprehensive clinical information on cancer tumors, such as location, size and rate of growth.

What is the main incentive for promoting cancer patients’ management based on smart imaging? 

It promises to enable personalized cancer patient management by providing the medical practitioner with a non-invasive and non-destructive tool to detect, stage and follow up cancer tumors in a standardized and reproducible manner. Furthermore, applying smart imaging that provides valuable disease-related information throughout the management pathway of cancer patient will eventually result in reducing the growing burden of health-care costs related to cancer patients’ treatment.

Let’s briefly review the segments that are common to all cancer patients’ pathway: screening, treatment and costs.

 

Screening for cancer: It is well known that one of the important factors in cancer treatment success is the specific disease staging. Often this is dependent on when the patient is diagnosed as a cancer patient. In order to detect cancer as early as possible, i.e. before any symptoms appear, leaders in cancer patients’ management came up with the idea of screening. To date, two screening programs are the most spoken of: the “officially approved and budgeted” breast cancer screening; and the unofficial, but still extremely costly, prostate cancer screening. After 20 years of practice, both are causing serious controversies:

In trend analysis of WHO mortality data base [1], the authors, Autier P, Boniol M, Gavin A and Vatten LJ, argue that breast cancer mortality in neighboring European countries with different levels of screening but similar access to treatment is the same: “The contrast between the time differences in implementation of mammography screening and the similarity in reductions in mortality between the country pairs suggest that screening did not play a direct part in the reductions in breast cancer mortality”.

In prostate cancer mortality at 11 years of follow-up [2],  the authors,Schröder FH et. al. argue regarding prostate cancer patients’ overdiagnosis and overtreatment: “To prevent one death from prostate cancer at 11 years of follow-up, 1055 men would need to be invited for screening and 37 cancers would need to be detected”.

The lobbying campaign (see picture below)  that AdmeTech (http://www.admetech.org/) is conducting in order to raise the USA administration’s awareness and get funding to improve prostate cancer treatment is a tribute to patients’ and practitioners’ frustration.

 

 

 

Treatment: Current state of the art in oncology is characterized by a shift in  the decision-making process from an evidence-based guidelines approach toward personalized medicine. Information gathered from large clinical trials with regard to individual biological cancer characteristics leads to a more comprehensive understanding of cancer.

Quoting from the National cancer institute (http://www.cancer.gov/) website: “Advances accrued over the past decade of cancer research have fundamentally changed the conversations that Americans can have about cancer. Although many still think of a single disease affecting different parts of the body, research tells us through new tools and technologies, massive computing power, and new insights from other fields that cancer is, in fact, a collection of many diseases whose ultimate number, causes, and treatment represent a challenging biomedical puzzle. Yet cancer’s complexity also provides a range of opportunities to confront its many incarnations”.

Personalized medicine, whether it uses cytostatics, hormones, growth inhibitors, monoclonal antibodies, and loco-regional medical devices, proves more efficient, less toxic, less expensive, and creates new opportunities for cancer patients and health care providers, including the medical industry.

To date, at least 50 types of systemic oncological treatments can be offered with much more quality and efficiency through patient selection and treatment outcome prediction.

Figure taken from presentation given by Prof. Jaak Janssens at the INTERVENTIONAL ONCOLOGY SOCIETY meeting held in Brussels in October 2011

For oncologists, recent technological developments in medical imaging-guided tissue acquisition technology (biopsy) create opportunities to provide representative fresh biological materials in a large enough quantity for all kinds of diagnostic tests.

 

Health-care economics: We are living in an era where life expectancy is increasing while national treasuries are over their limits in supporting health care costs. In the USA, of the nation’s 10 most expensive medical conditions, cancer has the highest cost per person. The total cost of treating cancer in the U.S. rose from about $95.5 billion in 2000 to $124.6 billion in 2010, the National Cancer Institute (www.camcer.gov) estimates. The true sum is probably higher as this estimate is based on average costs from 2001-2006, before many expensive treatments came out; quoting from www.usatoday.com : “new drugs often cost $100,000 or more a year. Patients are being put on them sooner in the course of their illness and for a longer time, sometimes for the rest of their lives.”

With such high costs at stake, solutions to reduce the overall cost of cancer patients’ management should be considered. My experience is that introducing smart imaging applications into routine use could contribute to significant savings in the overall cost of cancer patients’ management, by enabling personalized treatment choice and timely monitoring of tumors’ response to treatment.

 

 References

  1. 1.      BMJ. 2011 Jul 28;343:d4411. doi: 10.1136/bmj.d4411
  2. 2.      (N Engl J Med. 2012 Mar 15;366(11):981-90):

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