This great nation cannot afford to allow its citizens to suffer needlessly from the lack of proper medical care.

Those aren’t my words. That’s a thought expressed by President Harry S Truman in his 1948 State of the Union address—one that still resonates today.

As we work with a new administration and Congress to improve the delivery of healthcare to all Americans, we should be mindful that effective healthcare reform requires more than expanding coverage. Our entire system is suffocating under the unsustainable weight of $2.2 trillion a year in spending.

 Reining in these costs is crucial if we are to create the opportunity for universal access to medical care.

During my 30-year career in healthcare, I have worked passionately to ensure that care is accessible to as many people as possible. But that goal is unattainable if healthcare is 

The United States spends more than $7000 per year, per person, on healthcare1—twice the amount of most other developed countries (

). However, our quality of care is no better. Our life expectancy and overall health status are similar to those in other developed countries.

 In short, we’re paying twice as much for healthcare, but receiving little incremental value.

If the public and private sectors do not collaborate to fix this problem, our only alternative may be to move to a government-based, single-payer model—thereby reducing choice and stifling innovation. Without action, healthcare could become the next national housing or financial crisis. For millions of Americans, it already is.

To restore balance, we need to align our spending to levels consistent with those in other developed countries. That means reducing costs by $1 trillion a year—about 50%. The savings will reposition America’s health system to stimulate innovation and extend coverage for the uninsured.

In the past, savings efforts often focused on shifting costs and reducing physician reimbursement and availability—ultimately a counterproductive exercise with rapidly diminishing returns. Today, we must take a more enlightened approach, focusing on improved efficiency and evidence-based guidelines to deliver sustainable reductions in our baseline spending levels. A more efficient cost structure will free resources for investing in innovation and extending access to uninsured populations. Healthcare reform must focus on reducing the size of the bill, instead of who pays the bill.

Three principles will guide successful healthcare reform:

• First, we need to keep it simple. Complex solutions always fail.

• Second, we need to phase in incremental changes. Revolutionary reform is rejected by our society.

• Finally, and most importantly, we must carefully define the roles of the private sector and the government. When those responsibilities are blurred,

failure is inevitable. We need to capitalize on the

—The government’s strength is to promulgate and regulate.

—The private sector’s strength is to operate and innovate.

Policy debates in the 1990s underscored the perils of violating these principles. An early plan for healthcare reform—a major restructuring that involved more than 200 government agencies&mdash;was doomed by its scope and complexity. A major effort at Medicare reform in 1995 also failed, due in part to a lack of public buy-in to what appeared to be revolutionary changes in a core entitlement program.

Managed care companies crossed into the government’s realm in the mid-1990s when their cost-cutting measures made them the arbiter of healthcare protocols. They achieved some success in taming inflation, but the backlash was severe. “Anti—managed care” regulations were promulgated, health plans were rendered less effective, and costs began to rise again. The reality is that our culture does not trust the private sector as an arbiter of healthcare. Rules for healthcare delivery need to be established by the government after open discussion and public scrutiny.

We need to stimulate meaningful reform by casting the private and public sectors in appropriate roles. This can be accomplished by focusing on two primary areas:

• Use of protocol-driven, evidence-based medicine to drive transparency and accountability throughout the healthcare system. Wiring healthcare will be a key enabler for this reform.

• A national imperative to educate the public about healthy lifestyles that focuses on prevention, rather than fixing problems after the fact.

Protocol-Driven, Evidence-Based Healthcare System

To improve outcomes and reduce costs, medicine must be practiced as both a science and an art—complementing, but not replacing, the professional judgment of an experienced physician. There is good evidence that adherence to proven standards can improve healthcare outcomes. For example, hospitals in Michigan and Maryland have reduced their intravenous line infection rates&mdash;a common complication for intensive-care patients&mdash;to virtually zero by requiring caregivers to follow a checklist when inserting the line.

 This protocol has saved more than 1000 lives and $175 million in the hospitals where it has been instituted.4 Sound clinical practice based on medical evidence can lead to savings and better patient care.

There are four key opportunities for healthcare savings that will accelerate and build on evidence-based medicine: deploying information technology, improving medication compliance, reducing liability costs, and implementing Medicare reform.

Transitioning to a protocol-driven healthcare system requires measurement and information exchange. That means wiring the healthcare system to coordinate care, measure results, and drive accountability. By some independent estimates, we could save $162 billion a year from improved efficiency and precision in care, and we could begin measuring everything from the quality of hospitals to the individual performance of caregivers.

return for investing in electronic medical systems is huge—approximately 20 times the purchase cost. However, only about 20% of hospitals and 15% of physicians now have access to these systems.

Offering incentives to doctors who use electronic prescribing is an important step in this direction. In an era when children are Internet savvy, it is  inexcusable that doctors write paper prescriptions, a process that creates opportunities for error. Electronic prescribing reduces costs by eliminating errors and by helping physicians find the highest-value medications for their patients. Pharmacy care is the first line of defense in treating nearly 90% of newly diagnosed chronic and complex medical conditions—conditions that account for 96% of all prescription drug spending and 75% of all medical spending. America’s pharmacy system already is extensively wired, so it is well positioned to serve as a catalyst for technology reform throughout the healthcare system. Over time, a fully wired infrastructure will become the engine that drives a cascading series of innovations that will spread across the healthcare system to improve clinical and financial outcomes.

We could save another $177 billion by improving medication compliance and reducing errors.

 Doctors are well paid to choose a course of treatment, but their advice is too often misunderstood by patients or ignored. Poor adherence to treatment can have expensive consequences for the patient’s health and the costs of care.

 In the case of diabetes, only 7% of patients have adequate control over the primary factors that could mitigate their condition and allow them to live a healthy and productive life—blood sugar, blood pressure, and cholesterol levels.

 Poor compliance with diabetes treatments may lead to blindness, renal failure, amputations, and other complications that magnify the condition’s clinical and financial costs.

The burden of improving compliance is not the patient’s alone, of course. Research shows that it currently takes 17 years on average from the time a medical protocol is proven effective until it becomes the standard of care.

 The lag between evidence and practice was underscored recently in Medco’s research on setting the proper dose for warfarin therapy based on a patient’s genetic profile. In August 2007, the US Food and Drug Administration (FDA) changed the drug’s label to recommend using a genetic test to guide dosing and reduce the risk of serious bleeding or clotting—a common problem that hospitalizes more than 1 in 5 patients in the early months of warfarin therapy. However, fewer than 1% of doctors recently contacted by Medco said they had heard of the test or the FDA recommendation. More rapid adoption of evidence-based protocols can improve the quality of care and eliminate costs associated with ineffective treatments.

Address Medical Liability and Defensive Medicine

Wiring healthcare will support tort reform by accelerating the use of scientifically sound protocols in medical practice. The adoption of evidence-based protocols will promote transparency and reduce the need for “defensive medicine” tied to unnecessary tests and procedures. The healthcare system could save up to $200 billion annually by eliminating the excessive costs associated with defensive medicine and liability insurance.

 Doctors who follow measurable, documented, scientifically sound protocols should be shielded from liability. Culturally, we must move beyond the belief that every negative health outcome must be “somebody’s fault.”

Medicare is considered “the third rail of politics,” despite the system’s pending insolvency by 2019. The policies and financial structures underpinning Medicare require intense review and provide many opportunities for savings. These opportunities are too numerous to review here in detail, but I can provide one example of an area of spending that will benefit profoundly from a national policy based on clinical evidence and common values—spending for end-of-life care.

Few people realize that 30% of Medicare spending, roughly $130 billion, relates to healthcare costs incurred by patients in the last year of life—often when there is no hope for recovery or improvement in their quality of life.

 After gathering input from doctors, ethicists, patient advocates, and other parties, the government needs to establish an evidence-based policy for end-of-life care to help everyone involved—families, physicians, and patients. This does not mean withholding or rationing essential care. It is not the role of the government or the private sector to pass judgment on hope.

Promoting Healthy Lifestyles Through Prevention and Wellness

More than $300 billion in annual healthcare costs could be saved if we embrace wellness as a national priority. For example, more than 10% of our overall medical spending—$275 billion&mdash;comes from self-inflicted conditions linked to obesity and smoking.

 Another $38 billion of national healthcare costs are related to drug and alcohol abuse.

 These are behavioral and cultural issues that will require education and lifestyle modification, and they represent only a fraction of the healthcare savings we could achieve by converting to a culture of wellness.

We need inspired and credible political leadership to advance this urgent public health priority. Most of us can remember how government-led campaigns changed behavior through vivid imagery that became etched into our memories: Smoky Bear’s reminder that “Only you can prevent forest fires”; the crash-test dummies serving as a testimonial for seat belt safety by informing us that “You can learn a lot from a dummy”; the haunting image of a stoic Native American’s tear caused by roadside littering, combined with a call to action, “People start pollution, and people can stop it”; and the sizzle of fried egg as we were advised that “This is your brain on drugs.” These campaigns succeeded in shaping America’s conscience and, over time, changing our behavior.

The private sector cannot be expected to lead behavioral and cultural change on the required scale, although we’ve seen employers and health insurers make valiant attempts to encourage wellness and preventive care. Such initiatives are likely to succeed only if wellness becomes a national imperative— something that will require strong political leadership and a renewed sense of personal accountability.

An Opportunity We Can’t Afford to Ignore

Widespread adoption of evidence-based protocols and healthy lifestyles provide a combined opportunity to reduce up to $1 trillion of current healthcare spending (

). Combined with bold policy leadership, this change will permanently alter the slope of healthcare cost increases—without squelching innovation and choice.

Healthcare reform is one of our most urgent domestic challenges. The payoff for doing it right is huge. The penalty for failure will be a financial burden for generations to come. Squabbling over “who pays the bill?” instead of asking “why is the bill so high?” fails to address root-cause problems. In the end, we all pay the bill—and it’s currently 2 times higher than it should be or could be. When healthcare is inaccessible, everyone suffers&mdash;and America deserves better.

 From Medco Health Solutions, Inc, Franklin Lakes, NJ.

Mr Snow is the Chairman and CEO of Medco Health Solutions, Inc.

David B. Snow, Jr, Chairman and CEO, Medco Health Solutions, Inc, 100 Parsons Pond Dr, Franklin Lakes, NJ 07417. E-mail: 

1. Hartman M, Martin A, McDonnell P, Catlin A; National Health Expenditure Accounts Team. 

National health spending in 2007: slower drug spending contributes to lowest rate of overall growth since 1998. 

2. Organization for Economic Cooperation and Development. 

Health at a glance 2007. November 13, 2007. http://www.oecd.org/document/11/0,3343,en_2649_34631_16502667_1_1_1_1,00.html. Accessed January

3. Pronovost P, Needham D, Berenholtz S, et al. 

An intervention to decrease catheter-related bloodstream infections in the ICU. 

A man of considerable influence. Hopkins Medicine. Fall 2008. http://www.hopkinsmedicine.org/hmn/f08/circling.cfm. Accessed January 12, 2009.

5. Hillestad R, Bigelow J, Bower A, et al. 

Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. 

6. Institute for Health and Productivity Management. 

Following Doctor’s Orders. March 31, 2008. http://www.timeinc.net/fortune/services/sections/customprojects/sections/080331_PatientCompliance.pdf.

7. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. 

Impact of medication adherence on hospitalization risk and healthcare cost. 

Poor control of risk factors for vascular disease among adults with previously diagnosed diabetes. 

Information systems can prevent errors and improve quality. 

The Factors Fueling Rising Healthcare Costs 2006 [prepared for America’s Health Insurance Plans]. January 2006. http://www.pwc.com/Extweb/ncevents.nsf/docid/ 909DE7BFE7C77C90802571F40035661D/$file/ahip-factors_fueling_rising_hc-costs.pdf. Accessed April 14, 2009.

Trends in Medicare payments in the last year of life. 

12. US Department of Health and Human Services. 

Medicare trustees report shows serious financial status of Medicare program [press release]. March 25, 2008. http://www.hhs.gov/news/press/2008pres/03/20080325a.html. Accessed January 7, 2009.

13. Centers for Disease Control and Prevention. 

Obesity and overweight: economic consequences. May 22, 2007. http://www.cdc.gov/nccdphp/dnpa/obesity/economic_consequences.htm. Accessed January 9, 2009.

14. Centers for Disease Control and Prevention. 

Annual smokingattributable mortality, years of potential life lost, and productivity losses—United States, 1997-2001. 

15. Office of National Drug Control Policy. 

The economic costs of drug abuse in the United States: 1992-2002. December 2004. http://www.whitehousedrugpolicy.gov/publications/economic_costs. Accessed January 12, 2009.

Health care costs of alcohol. 2006. http://www.marininstitute.org/alcohol_policy/health_care_costs.htm. Accessed January 9, 2009.

Articles

COMMENTARY

Widespread adoption of evidence-based protocols and healthy lifestyles provide a combined opportunity to reduce up to $1 trillion of current healthcare spending.

Prescription for National Healthcare Reform

 To test the efficiency and cost-effectiveness of interventions aimed at enhancing laboratory monitoring of medication.

 Patients of a not-for-profit, group-model HMO were randomized to 1 of 4 interventions: an electronic medical record reminder to the clinician, an automated voice message to patients, pharmacy-led outreach, or usual care. Patients were followed for 25 days to determine completion of all recommended baseline laboratory monitoring tests. We measured the rate of laboratory test completion and the cost-effectiveness of each intervention. Direct medical care costs to the HMO (repeated testing, extra visits, and intervention costs) were determined using trial data and a mix of other data sources.

The average cost of patient contact was $5.45 in the pharmacy-led intervention, $7.00 in the electronic reminder intervention, and $4.64 in the automated voice message reminder intervention. The electronic medical record intervention was more costly and less effective than other methods. The automated voice message intervention had an incremental cost-effectiveness ratio (ICER) of $47 per additional completed case, and the pharmacy intervention had an ICER of $64 per additional completed case.

 Using the data available to compare strategies to enhance baseline monitoring, direct clinician messaging was not an efficient use of resources. Depending on a decision maker’s willingness to pay, automated voice messaging and pharmacy-led efforts can be efficient choices to prompt therapeutic baseline monitoring, but direct clinician messaging is probably a less efficient use of resources.

Patients of a not-for-profit, group-model HMO were randomized to 1 of 4 interventions: an electronic medical record reminder to the clinician, an automated voice message to patients, pharmacy-led outreach, or usual care.

The electronic medical record intervention was more costly and less effective than other

Depending on a decision maker’s willingness to pay, pharmacy-led outreach or automated

	voice messages to patients can be cost-effective methods to enhance laboratory monitoring of

	medications at initiation of therapy compared with usual care.

Recent studies indicate that laboratory monitoring of medications at initiation of therapy is below the level recommended by guidelines, with as many as 39% of patients not receiving recommended testing.

 Lack of monitoring is a concern because of potential adverse events (eg, hyperkalemia associated with inhibitors of angiotensin) and because of failure to achieve therapeutic benefit due to inadequate blood levels of medication. Additionally, failure to establish baseline levels makes it difficult to determine a patient’s trends in laboratory values.

These concerns regarding patient safety and clinical effectiveness have led researchers to test methods to enhance laboratory-based medication monitoring. Several types of interventions (including pharmacyled efforts, electronic reminders to clinicians, and automated telephone call reminders to patients) to improve laboratory monitoring of medications at therapy initiation have been effective in randomized trials.

 Although all these interventions improve monitoring, the most efficient intervention methods are not clear, and no economic analyses have been done to inform policy makers in this area. Some efforts may be particularly resource intensive, but could be worth the added expenditure when the potential adverse outcome is severe. Without careful analysis of the balance between costs and benefits, one cannot determine which (if any) interventions ought to be funded by healthcare payers. The efficiency of alternative approaches to therapeutic monitoring is of growing importance to healthcare providers as this monitoring is now a focus of quality measurement.

 To help with decision making regarding laboratory-monitoring interventions, we undertook a preplanned costeffectiveness analysis of a randomized trial that tested several interventions aimed at enhancing laboratory monitoring of medication.

Complete details of the trial design are available elsewhere.

 The study was conducted at a not-for-profit, group-model HMO and was approved by its institutional review board. All HMO patients who had not received baseline laboratory tests (defined as within 6 months before or 5 days after a newly dispensed study medication) were randomized to 1 of 4 conditions: an electronic medical record reminder to the patient’s primary care provider (EMR arm), an automated voice message to patients (AVM), pharmacy team outreach (Pharmacy), or usual care (UC) (

). In the EMR intervention, a patient-specific electronic message was sent to the primary care clinician from the chair of the HMO’s patient safety committee stating that computer records indicated the patient had received a new medication, that laboratory monitoring was recommended, and the patient had not received the test(s) between 6 months before and 5 days after the dispensing. The message referenced internal and external guideline resources, recommended specific tests, and provided a sample letter the clinician could send to the patient. The AVM intervention included telephone messages advising the patient that laboratory tests were required for a medication the patient had received; the patient was advised that the testing had been ordered and could be completed at any HMO laboratory. The Pharmacy intervention began with a telephone call from a nurse in the pharmacy department to the patient to encourage laboratory testing. If the nurse successfully contacted the patient, a follow-up letter reminded the patient to obtain the laboratory test(s). If telephone contact was not successful, the nurse sent a letter suggesting that the patient go in for testing. If patients had questions or concerns about their medication during the contacts, a pharmacist was available for consultation.

Study medications (and lab tests required) were angiotensinconverting enzyme inhibitors or angiotensin receptor blockers (serum creatinine, serum potassium), allopurinol (serum creatinine), carbamazepine (aspartate aminotransferase [AST] or alanine aminotransferase [ALT], complete blood count, serum sodium), diuretics (serum creatinine, serum potassium), metformin (serum creatinine), phenytoin (AST/ALT, complete blood count), pioglitazone (AST/ALT), potassium supplements (serum potassium, serum creatinine), statins (AST/ALT), and terbinafine (AST/ALT, serum creatinine). The primary outcome was laboratory test completion, defined as the proportion of patients with all recommended baseline laboratory monitoring tests completed at 25 days after the intervention date. In the year before randomization, the laboratory-monitoring rates at the initiation of therapy (those who had initiated a study medication and had completed all recommended baseline laboratory testing) were similar in the study groups (about 60%). Other characteristics of the study groups also were similar, but the AVM group had a smaller proportion of female primary care physicians (24% vs ~40%). A total of 961 patients were included in the clinical trial. By day 25 after the intervention, 22.4% (53 of 237 patients) in the UC arm, 48.5% (95 of 196 patients) in the EMR arm, 66.3% (177 of 267 patients) in the AVM arm, and 82.0% (214 of 261 patients) in the Pharmacy arm all had completed recommended monitoring (P <.001). A total of 72 abnormal test results were found among the 961 patients (7.5%).

We followed best practice in economic evaluation as outlined by the US Public Health Service.

 Our economic analysis examined the incremental cost per additional case completed (defined as enrollees that had all guideline-specified laboratory tests completed) and the incremental cost per abnormal case detected. We calculated the incremental cost-effectiveness ratio (ICER) by dividing the difference in cost by the difference in cases completed (or abnormal cases detected); interventions with lower ICERs are a better value for the money. Interventions with a higher ICER also may be cost-effective, depending on a decision maker’s willingness to pay for each additional unit of effect. Interventions were ranked on cost, and dominated options (ie, more costly but less effective) were identified. To account for the uncertainty due to sampling variation in cost-effectiveness analysis, we plotted cost-effectiveness acceptability curves5; these curves show the probability of each intervention being cost-effective at a given willingness to pay for an additional completed case (or abnormal case detected). All analyses were conducted using STATA release 9.0 (StataCorp, College Station, TX) and Microsoft Excel 2003 (Microsoft, Redmond, WA).

Most of the cost data were collected directly from the trial, with some expert opinion based on formal data-gathering techniques as described below. The perspective of the analysis was the HMO. Thus, we included only direct medical care costs incurred by the HMO. The scope of the analysis included the costs within the 25 days after the intervention of (1) all recommended laboratory tests (including repeated testing), (2) extra visits associated with abnormal tests (validated from pharmacist chart review), and (3) performing the intervention. The analysis does not include potential offsets of poor outcomes averted (eg, lactic acidosis, liver toxicity) because those data were too sparse to answer those questions effectively. Also excluded from the analysis were development costs and patient costs, like travel time and copayments. To maintain consistency with the efficacy analysis, the primary outcome was the cost per completed case within 25 days of dispensing, with a secondary analysis of the cost per addition al enrollee with 1 or more abnormal laboratory tests within 25 days of dispensing.

 details the unit costs (ie, prices), activities, data sources, and resource assumptions used in the analysis. To improve generalizability to other systems, salary costs were taken from sources reflecting the prevailing wage rate in Portland, Oregon, with a fringe benefit rate of 30% and overhead rate of 20% added to fully allocate the costs. Laboratory testing costs come from the HMO’s laboratory accounting system and include patient intake, phlebotomy, testing, and reporting. Mailing costs were applied based on estimates for bulk mailing, and costs for clinic visits came from the HMO’s cost structure.

Resources used in the performance of all the interventions included chart review to ensure patient eligibility, tracking systems for patient follow-up, and noting the intervention delivery in the patient’s medical record. Additional tasks were study arm specific. In the Pharmacy arm they included time for mailings and outreach phone calls and their documentation. The AVM intervention required time to upload files. The cost of maintaining the automated telephone system was embedded in the vendor charge to the HMO. The EMR intervention costs included nurse time to send the message and clinician followup activities. Because existing EMR functionality was used to provide the EMR messages, no incremental programming resources were necessary to provide the intervention. The time (in minutes) taken to complete these tasks was recorded for a sample of patients in the Pharmacy, AVM, and EMR arms. To establish patient-level resource use, each patient in the appropriate arm was assigned an imputed value randomly from the sample, preserving the sample’s underlying (observed) distribution. Analyst time to create and maintain the patient lists from automated data were taken from the trial.

In the base-case analysis, formal interviews were undertaken with 13 HMO clinicians to estimate time (in minutes) for all aspects of responding to the EMR message: reading the message, ordering tests, and review and follow-up (for normal and abnormal results separately) on laboratory results. We then imputed patient-level resource use for these items by randomly selecting from a triangular distribution, using the expert’s estimates of the mode and range. Because they came from expert opinion, we undertook sensitivity analyses by assigning the low estimates and, separately, the high estimates of time required for ordering, review, and follow-up on normal and abnormal test results. Additionally, because of uncertainty regarding the frequency and type of contact for patients in the EMR arm (eg, telephone, letter, during visits), we undertook a sensitivity analysis that reduced the cost of contact in that arm.

presents the resources used per patient by arm (ie, unit costs from Table 1 multiplied by actual units of use). The increase in laboratory testing associated with each of the interventions is evident from changes in the average cost of testing ($18.65 for UC, vs $26.69, $32.44, and $40.93 for EMR, AVM, and Pharmacy, respectively); the cost for review and follow-up of both normal and abnormal test results followed a similar pattern. The average cost of patient contact (including chart review, notification tracking, and charting) was $5.45 in the Pharmacy arm. The EMR arm used about $7 ($3.18 + $3.77) for patient contact, while the AVM arm cost for patient contact came to $4.64 ($3.55 + $1.09).

 shows that enhanced effectiveness comes with increases in total cost. Pharmacy, the intervention with the greatest proportion of completed cases at 82 per 100 patients, is also the most expensive at $5160 per 100 patients, whereas the AVM arm yielded 66 completed cases per 100 patients at a cost of $4159. The EMR arm was “dominated,” because using a mix of UC (eg, for 40% of patients) and AVM (eg, for 60% of patients) would be both less expensive and more effective; the EMR strategy is therefore a suboptimal choice. The AVM intervention had an ICER of $47 per additional completed case; the Pharmacy arm had an ICER of $64 per additional completed case. At a lower level of willingness to pay for an additional completed case (eg, $40), UC was the intervention with the highest probability of being cost-effective; as willingness to pay increased, however, other interventions became more likely to be cost-effective.

 shows an examination of the cost-effectiveness of finding abnormal cases. There was a similar pattern of increasing effectiveness at higher cost, and the EMR intervention method was again dominated. As with completed cases, at a lower level of willingness to pay for finding an abnormal case (eg, $400), UC had the highest probability of being cost-effective, but above $600 AVM and Pharmacy became more likely to be cost-effective. The sensitivity analysis based on low and high estimates of time used in ordering, reviewing, and follow-up of normal and abnormal tests  showed no difference in ICER ranking and nearly identical results overall (eg, ICERs of $44 and $50 for the AVM arm vs $47 for the base-case analysis). The sensitivity analysis on the cost of contact for patients in the EMR arm revealed that, even if the cost of patient contact was reduced to zero (eg, the marginal cost might be close to zero if all contact took place during visits), the EMR arm would never be the optimal strategy.

 shows the cost-effectiveness acceptability curves. For example, at a willingness-to-pay level for a completed case between $47 and $64, AVM had the highest probability of being cost-effective, but the Pharmacy intervention had the highest probability of being cost-effective at greater levels. There is considerable uncertainty in the estimates of cost-effectiveness, particularly for abnormal case detection. For example, at levels of $800 and higher AVM had a probability of being cost-effective of about 0.32, while Pharmacy was cost-effective with a probability of about 0.50.

We found that direct-to-clinician messaging (EMR) to prompt therapeutic baseline monitoring is probably not an efficient use of resources. But our analysis indicates that depending on a decision maker’s willingness to pay for a completed case, both AVM and Pharmacy can be efficient choices. Deciding which choice is optimal requires additional information about the harms prevented from completing baseline monitoring; our analysis did not include these costs because of limitations in study budget and design. However, it is worth noting that compared with the ICER for the AVM arm, the ICER for the Pharmacy arm was almost 40% greater for the “cases completed” analysis, but was less than 10% greater for the “abnormal cases detected” analysis, suggesting that the relative efficiency of the Pharmacy intervention may increase when considering harms.

As noted, our findings are most useful for an efficiency comparison between the alternative methods in the analysis. The findings give less guidance to help with the decision about what (if any) program should be adopted; a cost-benefit or cost-utility analysis would be needed to answer that question. But an informal analysis can be made using the data in Table 3 and making assumptions about the cost and probability of events that might be avoided by a monitoring program. For example, if one assumes that the cost of an event avoided would be $10,000 (ie, short inpatient stay) and the probability of the event for those with an abnormal test is 5%, the willingness-to-pay amount to avoid the event would be at least $500 (ie, $10,000 × 5%); this example does not incorporate potential losses in health-related quality of life. Empirical work on the probability of abnormal tests leading to adverse events, the cost of those events, and the health benefits gained is necessary to further inform decision makers’ choices in this area. Reports of simulation modeling on the economics of preventing medication errors using information technology and pharmacy personnel indicate that health-related quality-of-life benefits (from avoiding medican tion errors) are a critical parameter; their inclusion reversed the estimates from negative to positive net benefits.6

Our intervention did not differentiate patients with regard to potential for poor outcomes; rather it was applied to all patients with a missing laboratory test at baseline. Another approach would be to administer the intervention to patients who are predicted to be at particularly high risk of poor outcomes. By targeting high-risk patients, the proportion of patients with a greater likelihood of adverse events (given an abnormal test) would increase. Such an approach might increase the efficiency of the strategies but would require identification of those patients, perhaps by using a risk score approach.

The clinical trial on which we based our economic analysis showed that each of the 3 interventions was superior to UC in improving monitoring at 25 days; patients in the EMR arm were 2.5 times more likely to have recommended monitoring.

 But when we included costs (ie, increase in clinician time associated with contacting the patient and follow-up), the EMR arm was shown to be relatively inefficient. Off-loading these tasks to others enhances the overall effects and lowers costs. We included the effort required by clinicians to review laboratory test results in our cost estimates. Another approach would have been to assume that clinicians can absorb the cost of reviewing the extra tests into their slack time. But as suggested by best practice guidelines in economic evaluation,

 we estimated the “opportunity cost” of providing the interventions. The concept of opportunity cost requires measurement of all resources necessary to produce an intervention, even if those resources may be viewed as free (eg, the effort required to review laboratory tests); this concept recognizes that doing one thing means that another thing cannot be done (because that opportunity is lost).

We found relatively high levels of certainty associated with willingness to pay for an additional completed case. At a willingness-to-pay level of less than $40 the probability of UC being the most cost-effective was about 0.9, and at a willingness-to-pay level greater than about $85, the Pharmacy intervention was the most cost-effective with a probability of 0.9. With intermediate levels of willingness to pay for a completed case (ie, $50-$65) AVM was the most cost-effective, but with less certainty (probability about 0.6). On the other hand, we found considerable uncertainty with the abnormal case detection analysis. No single intervention showed costeffectiveness with probability greater than 0.5 at a willingness to pay between $450 and $850. The uncertainty estimates are loosely analogous to a wide confidence interval (see van Hout et al

 for further details) and point out the lack of precision in our estimates. More precise estimates require further data collection.

Our results were not sensitive to the estimates of time required to order, review, and follow-up on tests, suggesting that additional data collection should focus on other areas like estimating the potential cost-offsets from avoiding poor outcomes.

Some items we included in the performance of these interventions, for example, the individual chart review of each patient prior to intervention to ensure eligibility (ie, we did not rely entirely on electronic data), may not have been strictly necessary. Excluding these potentially protocol-driven costs may change the relative cost-effectiveness of the interventions. We felt that this was an important feature to include, however, because the effectiveness of the intervention was based on that level of rigor, and because the HMO would likely not be willing to implement any of the programs without that double-check. Additionally, we included patient contact costs in the EMR arm for all patients, but this may have overcounted costs in that arm to the extent that the rate of contact was lower. But our sensitivity analysis on that variable indicated that even if the contact costs were reduced to zero the EMR arm would never have been the optimal strategy.

The clinical trial allowed us to evaluate strategies that would off-load clinicians’ work (ie, AVM and Pharmacy arms) as compared with more intrusive strategies (the EMR arm). Because we did not set out to balance resources available in the intervention arms, we cannot determine whether the effect and cost difference we observed was solely based on the intervention delivery mechanism, the intensity of the intervention, or other factors. Also, although a laboratorymonitoring protocol was available and communicated to all clinical staff, clinicians in the EMR arm may have been more likely to exercise clinical judgment on a case-by-case basis and thus not intervene for every patient. Our analysis assumes that all patients who received interventions were at the same risk of poor outcomes. Because we did not measure outcomes in our analysis, to the extent that the clinical judgment exercised by EMR clinicians improved the likelihood of interventions for high-risk patients (thus improving their group’s potential to benefit), our analysis underestimated the true effect in the EMR arm.

Our study has limitations. The clinical trial on which it was based was undertaken in a single HMO, so the effect of the interventions (especially EMR) may not be completely generalizable to other practice settings. Additionally, the parent study was not designed to determine whether the interventions led to improved detection of abnormal baseline laboratory test results or significant changes in patient care and outcomes. Our findings in this cost-effectiveness analysis help to put the clinical trial results in a decision-making context. We think a hybrid approach to the intervention may be a particularly useful approach. For example, one that includes AVM followed by Pharmacy outreach for nonresponders may prove to be efficient.

The efficient use of health information technology is of great interest to clinicians and healthcare payers, and our study is one of the first to estimate the relative cost-effectiveness of these technologies. Using automated telephone technology has been shown to have promise in many clinical areas, for example post-inpatient discharge,

 enhancing immunization rates in children,

 but the cost-effectiveness of these systems is less clear. The use of automated medical records has been the subject of several systematic reviews.

 Like automated telephone technology, the EMR systems have promise, but the economic benefits are less certain. Our study shows that providing clinical decision support directly to clinicians using EMR technology is more costly and less effective than automated telephone calls for increasing laboratory monitoring. This finding is particularly of interest given (1) that we did not include costs for programming of EMR messages because existing EMR functionality was used and (2) the “sunk cost” of the EMR was not included in our analysis because from the HMO’s perspective the EMR technology was already owned. But it should be noted that the costs of chart review and tracking activities would likely have been substantially higher in a health system that lacked an EMR. Our study may serve as a cautionary example of when EMR-based intervention methods are not the most efficient choice.

Depending on one’s willingness to pay, both AVM and a pharmacy-led intervention may be efficient methods to enhance the level of laboratory monitoring of medications at initiation. Our analysis gives guidance, but further empirical work to estimate the cost and probability of adverse events associated with abnormal laboratory tests is important to further inform funding decisions.

: From the Center for Health Research (DHS, ACF, NAP, XY, MMR), Kaiser Permanente and Northwest Permanente (ACF), Portland, OR; the Institute for Health Research (MAR, DJM), Kaiser Permanente Colorado, Denver; Harvard Medical School (SRS, SBS) and Harvard Pilgrim Health Care (SRS, SBS), Boston, MA. Funding Source: The study was funded by Kaiser Permanente’s Garfield Memorial Fund.

: The authors (DHS, ACF, NAP, XY, MMR, MAR, DJM, SRS, SBS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: Concept and design (DHS, ACF, NAP, MMR, MAR, DJM, SRS, SBS); acquisition of data (DHS, ACF, XY, MMR); analysis and interpretation of data (DHS, ACF, NAP, XY, MMR, MAR, DJM, SRS, SBS); drafting of the manuscript (DHS, MMR); critical revision of the manuscript for important intellectual content (DHS, ACF, NAP, MAR, DJM, SRS, SBS); statistical analysis (DHS, NAP, XY, SBS); provision of study materials or patients (DHS); obtaining funding (DHS, ACF); administrative, technical, or logistic support (DHS, ACF, MMR); and supervision (DHS, ACF, SBS).

: David H. Smith, RPh, PhD, Center for Health Research, Kaiser Permanente, 3800 N Interstate Ave, Portland, OR 97227. E-mail: 

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 Improving laboratory monitoring at initiation of drug therapy in ambulatory care: a randomized trial. 

2. Feldstein AC, Smith DH, Perrin N, et al.

 Improved therapeutic monitoring with several interventions: a randomized trial. 

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http://web.ncqa.org/Portals/0/HEDISQM/Archives/2007/MeasuresList.pdf

4. Gold M, Siegel J, Russel L, Weinstein M

Cost-effectiveness in Health and Medicine

. New York, NY: Oxford University Press; 1996.

5. van Hout BA, Al MJ, Gordon GS, Rutten FF.

 Costs, effects and C/Eratios alongside a clinical trial. 

 Modelling the expected net benefits of interventions to reduce the burden of medication errors. 

7. Lumley T, Kronmal RA, Cushman M, Manolio TA, Goldstein S.

 A stroke prediction score in the elderly: validation and Web-based application. 

 The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. 

 Using an interactive voice response system to improve patient safety following hospital discharge. 

10. Lieu TA, Capra AM, Makol J, Black SB, Shinefield HR

. Effectiveness and cost-effectiveness of letters, automated telephone messages, or both for underimmunized children in a health maintenance organization. 

11. Vollmer WM, Kirshner M, Peters D, et al.

 Use and impact of an automated telephone outreach system for asthma in a managed care setting. 

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 Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. 

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CLINICAL

Although some interventions may enhance medication safety, an electronic medical record reminder to providers may not be an efficient use of resources.


Improving Laboratory Monitoring of Medications: An Economic Analysis Alongside a Clinical Trial

To assess the time needed to reach the “doughnut hole” (DH) and catastrophic coverage (CC) periods and to identify prescription drug use patterns among patients eligible for the standard Medicare Part D drug benefit.

Data for all prescription drugs dispensed to subjects over age 65 years from November 1, 2006, through February 29, 2008, were obtained from 2 large retail pharmacy chains. Kaplan-Meier estimates of survival curves 

were used to assess the time to reach the DH and CC thresholds.

Of all 2007 Medicare Part D standard benefit patients in our sample, 18.5% reached the DH, but only 11.6% of those patients reached the CC threshold by the end of 2007. Patients who did not reach the DH in 2007 filled an average of 2.13 prescriptions per month. Patients who reached the DH but did not reach the CC threshold filled an average of 4.86 and 4.40 (9.47% decrease; 

 <.001) prescriptions per month during the initial coverage and DH periods, respectively. Patients who reached the CC threshold filled an average of 7.59, 8.38, and 7.86 prescriptions per month during the initial coverage, DH, and CC periods, respectively. Similar quantitative patterns were observed for patients with various chronic conditions.

 A sizable proportion of standard Medicare Part D drug program beneficiaries reached the DH. Prescription data can help predict the problems beneficiaries enrolled in the standard Medicare Part D drug program might face over time.

In this retrospective cohort study we identified prescription drug use patterns among patients eligible for the standard Medicare Part D drug benefit.

Almost 20% of standard Medicare Part D program beneficiaries reached the “doughnut hole” in 2007.

More than 11% of those patients who did enter the doughnut hole reached the catastrophic coverage threshold.

Patients who reached the doughnut hole but did not reach catastrophic coverage filled, on average, about 10% fewer scripts in the doughnut hole period.

The standard Medicare Part D drug coverage is divided into 3 consecutive phases: the initial coverage (IC), the “doughnut hole” (DH), and the catastrophic coverage (CC). Depending on the patient’s health plan, during the IC phase a patient could pay a deductible in addition to approximately 25% of the drug costs. During the DH or the gap in coverage, most patients must pay 100% of the drug costs out of pocket. During the CC phase, a patient could pay approximately 5% of the drug costs. Sixty-five percent of Part D beneficiaries in 2007 were covered by the standard benefit or a benefit that was actuarially equivalent.

According to a projection by the Actuarial Research Corporation for the Kaiser Family Foundation, more than 3 million of the 24.2 million full-year Part D enrollees reached the DH in 2007. Despite its large scope, very little is known about the dynamic of reaching the DH and CC thresholds over time. The main objective of our analysis was to assess the time taken to reach the DH and the CC thresholds in the general elderly population and in the subpopulations of patients with specific chronic conditions eligible for the Medicare Part D standard benefit in 2007. As part of this analysis, we estimated the percentage of patients that had reached the DH and CC by different time points in 2007.

There is debate about the magnitude of the impact of the DH and the CC on prescription drug use in the Medicare Part D population.

 Particularly, it is unclear whether patients tend to reduce their drug consumption after reaching the DH (because during this stage they pay the full cost of medications) and whether they increase consumption back to its original level after crossing the CC threshold. Hence, the second objective of this analysis was to study how the DH and the CC affect patient refill behavior.

Data for all prescription drugs dispensed to subjects over age 65 years from November 1, 2006, through February 29, 2008, were obtained from 2 large retail pharmacy chains. These 2 pharmacies together operate in a total of 50 states and the District of Columbia, and represent more than 4152 community pharmacies nationwide. The data contained all individual pharmacy customers regardlessof their insurance status and their prescription drug activity for every prescription filled at these national chains. Detailed drug plan information was not available for all patients in our data, but a Medicare Part D identifier was included. Diagnosis information was not available in the database.

Patients sometimes use more than 1 pharmacy chain to fill prescriptions. This study included only patients who were continual customers of the same pharmacy chain throughout 2007 and required that patients had minimal dispensing activity within the same pharmacy chain in the last 2 months of 2006 and the first 2 months of 2008. Patients who were eligible for Medicaid (ie, dual-eligibles) or received a lowincome subsidy were excluded from this analysis because their Medicare Part D benefit did not have a coverage gap.

The analysis was done on the full sample in addition to being performed separately for patients who were taking drugs typically used for the following medical conditions: hypertension, depression, asthma, diabetes, hyperlipidemia, stroke or heart attack, acid reflux disease, and osteoporosis.

Our data included the Medicare Part D identifier but not the indicator of standard Medicare Part D benefits. To identify patients with Medicare Part D standard benefits, we used an algorithm that considered the Medicare Part D benefits design for 2007, medication costs, patients’ out-of-pocket payments, and dispensing patterns.

The standard benefits for 2007 included a $265 deductible, a coinsurance payment of 25% up to $2400 in total drug costs, and a gap in coverage between $2400 and $5451 during which enrollees paid 100% of their drug costs. After enrollees had incurred $3850 in out-of-pocket expenses, they qualified for CC and paid 5% of drug costs, but not less than $2.15 for generic drugs or $5.35 for brand-name drugs.

According to these rules, we identified patients covered by the Medicare Part D standard benefit as those who paid 25% or less of drug costs at the beginning of 2007, excluding patients who had low copayments equivalent to the benefit for the low-subsidy Medicare population. We identified the end of the IC period and the beginning of the DH period by a jump in patient copayments to 90% or more of the drug cost. We identified the date of reaching the CC threshold by a drop in patient copayment to a maximum of 5% or less of the drug cost or $2.15 for generic drugs or $5.35 for brand-name drugs. (Many patients had the secondary insurance, which paid some additional portion of the drug costs.)

Kaplan-Meier estimates of survival curves

 were used to assess the time to reach the DH and CC thresholds. Times to reach the DH and CC thresholds were calculated as the number of days from January 1, 2007, to the date of reaching these thresholds. These outcomes were censored at 360 days for patients who did not reach the DH or CC by December 31, 2007.

We also evaluated the effects of reaching the DH and CC on patient drug consumption. For this analysis, we divided all patients into 3 cohorts. The first cohort included only patients who did not reach the DH in 2007; the second cohort included patients who reached the DH but did not reach the CC; and the third cohort included patients who reached the CC after reaching the DH. We calculated the average number of fills per 30 days for every patient in each coverage period (IC, DH, and CC). (The same calculations also were performed based on the days’ supply outcome, and quantitatively similar results were obtained [data not shown].) A multivariate generalized linear model with the negative binomial distribution and log link function was used to estimate changes in the average fill rates during the DH and CC periods compared with the average fill rate in the IC period. The generalized estimating equations

 approach was used to account for the repeated measurements on the same patient in consecutive time segments. A first-order autoregressive structure was specified for the working correlation matrix.

Of the 951,525 patients who were age 65 years and older and who during 2007 were continual customers of the 2 pharmacy chains used in this study, 161,899 (17.0%) were identified as Medicare Part D recipients. Of those, 96,579 patients (59.7%) were identified to be eligible for the standard Medicare Part D benefit, and they constituted the primary study cohort.

The mean age of the primary study population was 77.0 years (±7.3 years) and 65% were women. A smaller group of patients (16.3%) did not take any drugs for the chronic conditions under consideration, and 27.9%, 27.2%, and 28.6% of patients took drugs for 1, 2, and 3 or more of those conditions, respectively. 

We estimated that of all the patients covered by Medicare Part D standard benefit in 2007, 18.5% reached the DH by the year’s end. These corresponding numbers were considerably higher for patients with the chronic conditions under consideration—varying from 25.2% for patients taking antihypertensive drugs to 49.3% for patients taking drugs for reducing the risk of heart attack or stroke (

Of those patients who did enter the DH, only 11.6% reached the CC threshold by the end of 2007. The numbers were again considerably higher for patients with the chronic conditions under consideration, reaching 17.0% for patients with acid reflux disease and 16.8% for patients taking antidepressants (

). Patients who did not reach the DH in 2007 (cohort 1) filled,on average, 2.13 prescriptions per 30 days (

).  Patients who reached the DH but did not reach the CC (cohort 2) filled, on average, 4.86 and 4.40 (9.47% decrease; 

 <.001) prescriptions per 30 days in the IC and DH time periods, respectively. The comparable decrease in patient drug consumption (ranging from 8.95% for patients taking antihypertensive drugs to 10.66% for patients taking acid reflux disease drugs) was observed for all subgroups of patients with the chronic conditions under consideration. Patients who reached the DH and then reached the CC (cohort 3) filled, on average, 7.59, 8.38, and 7.86 prescriptions per 30 days in the IC, DH, and CC time periods, respectively. These figures reflect a 10.41% increase in the DH period compared with the IC period (

 = .001) and a 3.56% increase in the CC period compared with the IC period (

 = .006). Quantitatively we observed the same pattern for all subgroups of patients with chronic conditions (Table).

Patients in cohorts 1, 2, and 3 had, on average, 1.58 (±1.18), 2.85 (±1.20), and 3.64 (±1.40) chronic conditions, respectively.

The passage of Medicare Part D drug benefit has provided improved access to prescription drugs for Medicare beneficiaries. In 2007 the number of people enrolled in the Medicare Part D drug plans was estimated at 25 million.

 Our study focused on 3 features of Medicare Part D drug benefit: the IC, DH, and CC. Our findings suggest that only a portion of the enrollees reached the DH, and a smaller portion of those who reached the DH reached the CC threshold.

We also found how coverage or the lack of it affected prescription fill patterns. Patients who did not reach the DH filled, on average, 2.73 fewer prescriptions a month than those who did reach the DH. These patients were healthier in comparison, having fewer chronic conditions. They also might have delayed reaching the DH by reducing their essential and/or marginally useful medication consumption.

 Patients who reached the DH but not the CC filled, on average, fewer prescriptions and increased their out-of-pocket costs while in the DH. If this reduction in drug consumption affected the necessary medication, then it could have adversely affected the patients’ health outcomes, especially for those with chronic conditions.

Patients may use different strategies to try to mitigate the effects of the DH. They may switch from brand-name drugs to generics or over-the-counter medications, use pharmacy coupons, or obtain drug samples.

 Although a majority of patients prefer to use the same pharmacy on a regular basis, some patients may start shopping for pharmacies that offer low-price prescription drugs. Other patients may start using more drastic measures—stretching a prescription supply by splitting pills, taking them every other day, or even going without them entirely.

This retrospective cohort study found that a sizable proportion of standard Medicare Part D drug program beneficiaries reached the “doughnut hole.”

May 2009

Prescription for National Healthcare Reform

Improving Laboratory Monitoring of Medications: An Economic Analysis Alongside a Clinical Trial

Assessment of Drug Consumption Patterns for Medicare Part D Patients

Home Telemonitoring for Respiratory Conditions: A Systematic Review

Is Patient Satisfaction Influenced by the Intensity of Medical Resource Use by Their Physicians?

The Effectiveness of Diabetes Care Management in Managed Care

Physicians Respond to Pay-for-Performance Incentives: Larger Incentives Yield Greater Participation