Ambulatory Process of Care and Quality of Life Outcomes

A push towards more accurate evaluation of health care quality has become a major public policy goal in recent years. The Department of Health and Human Services now has a website ranking hospital quality and California has a Healthcare Quality Report Card. One problem with simple quality measures such as mortality or morbidity rates are that physicians who have a patient base with a lower initial health level are penalized—according to the scoring—for treating the patients who need care the most. For this reason, health care rankings have moved from outcome-based rankings to process-based rankings. For instance, the California Report Card ranks insurance plans by such factors as whether or not individuals with diabetes had an eye exam and the percentage of pregnant women who had a check-up visit 21-56 days after delivery.
One puzzling finding Kahn et al. (2007) encounters in their data is that ambulatory care centers with better process of care scores have patient with a worse health outcomes. Using a simple ordinary least squares (OLS) procedure would lead to the erroneous conclusion that more care leads to worse health. Of course, individuals who are sicker are specifically the patients who need to undergo the most procedures.
To control for this problem, Kahn and co-authors use an instrument variables method. The independent variable—the process of care variables—is instrumented with a structure of care variable. The structure or care is defined in the data by a set of medical organization dummy variables. This IV estimation technique will provide unbiased, precise estimates if the medical organization dummies are correlated with the process of care, but the structure of care only affects health outcomes as mediated by the process of care. I believe that this is a reasonable assumption.
Using this methodology, the authors do in fact find that better processes lead to better health outcome. Moving a patient from a process of care score in the lower quartile to a process of care score in the highest quartile will lead to approximately a 10% increase in the PCS health score.
While the conclusion of this paper seems obvious—better process of care scores lead to better health outcome—the authors have done a yeoman’s job of proving this empirically. Further, the paper casts doubt upon the conclusions from other studies which use OLS regressions to compare health interventions and health outcomes.


Kahn, Katherine; Diana M. Tisnado; John L. Adams; Honghu Liu; Wen-Pin Chen; Fang Ashlee Hu; Carol M. Mangione; Ronald D. Hays; Cheryl L. Damberg (2007) “Does Ambulatory Process of Care Predict Health-Related Quality of Life Outcomes for Patients with Chronic Disease?” Health Services Research 42 (1p1), 63–83.

Insurance Status and Access to Urgent Ambulatory Care Follow-up Appointments

In 2005, approximately 114 million visits were made by Americans to the hospital emergency departments. Of these, more than eighty percent concluded with a discharge and a recommendation for follow-up care. Receiving prompt and adequate post-ER care is imperative for the resolution of many illnesses and temporary disabilities. Is timely care available for these patients?
A study by Asplin, et al. (2005) and a subsequent paper by Neath and Carlin (2006) look at how easy it is to schedule an appointment after an ER visit. To collect the data, clinics were phoned by a graduate students posing as patients just released from a hospital emergency department. Callers had four (made-up) medical conditions: pneumonia, elevated blood pressure, vaginal bleeding in the first trimester, and symptoms of depression. The depression observations were excluded from the study because many primary care physicians do not feel qualified to treat depression.
In each call, the individual claimed to have either: 1) private insurance, 2) Medicaid insurance, 3) no insurance and could not pay, or 4) no insurance but would pay for the visit out-of-pocket. A call was deemed successful if an appointment was made within 7 days and the out-of-pocket payment for the appointment was $20 or less.
Results
Asplin, et al. preform a simple paired comparison in which the same clinics are compared where the only difference between the observations is the unit of insurance the phony patient had. Neath and Carlin directly incorporate other covariates - such as the medical condition, safety-net status of the clinic, city dummy variables, etc. We can see that the “overall success probabilities in Asplin et al. were distressingly low.” One also notices that it is much easier to get an appointment if one has private insurance, but these differences are less severe at “safety net” clinics. Finally, the authors note that the majority of clinics made no attempt to determine the severity of the caller’s condition. Having trained staff answering the phone calls and preforming triage is costly, but is likely worth the cost for patients needing immediate assistance. Put more concisely, Asplin states: “Financial screening is trumping medical triage.”

Source: Healthcare Econimist

Expected Value of Information and Decision Making in Health Technology Assessment

Health decision makers often have to decide whether to adopt a new health care intervention (e.g.: pharmaceuticals, new procedures, etc.) or keep the existing practice. If one assumes that the new intervention has positive but uncertain net benefits over the existing procedure, should the new technology be adopted?
A paper by Eckermann and Willan (2007) looks at this problem and create a theoretical framework to find which actions are best suitable for which situations. They claim that decision makers face three options:
adopt the new intervention without further research (A);
adopt the new intervention and undertake a trial (AT); or
delay the decision and undertake a trial (DT).
The authors adopt the notation of the expected net gain (ENG) where ENGA gives the expected gain from choose AT over A and ENGD gives the expected gain from choosing DT over A. ENGA represents the difference between the value of the trial (sample) information (assuming adoption) minus the cost of the trial. ENGD is the difference between the value of the trial (sample) information (assuming delay) minus the cost of the trial.
Delaying the decision allows time for more information to be collected, but creates direct costs (the cost of the trial) and opportunity costs (the cost of non-treatment of affected individuals during the delay). Deciding to adopt the technology and preform a trial has the benefit of providing more information to the decision maker but the additional direct cost of the trial as well as reversal costs (discussed later). The expected value of the of preforming the trial (expected value of sample information-EVSID) is calculated as follows.
EVSI=N[∫ -b*{f0(b)-f1(b)} db] = N*[E0(bb1(bb<0)]
The argument inside the integral is integrated between negative infinity and zero. The distributions f0 and f1 represent the predicted distribution of benefits at the present (0) and after the trial (1). The benefit level is given by the variable b, and N is the number of people affected by the disease. The information is only valuable if researchers find that there are more ‘bad’ outcomes than previously expected. If the new treatment proves safer than expected, choice A would have been optimal. Also, the trial is more valuable when the number of people affected with the disease, N, is larger since the decision will be a more important one to society.
Eckermann and Willian also add to the model the concept of the cost of reversal. After a new treatment is adopted, a subsequent reversal has costs. These costs include reversing information flows (e.g.: public health messages, changing med school training curriculum, etc.) and sunk cost investments in specific equipment or training. Taking into account these reversal costs makes option A seem (relatively) more attractive to the DT and AT cases since a reversal is impossible if a trial is not conducted.Using a cost benefit analysis, the following decision rules are established.
choose A when ENGA and ENGD<0;
choose AT when ENGA>0 and ENGD<0;
choose DT when ENGAD>0;
The authors give a more intuitive explanation as well.
AT is preferred where expected costs of reversal per patient are small relative to the expected distribution of net benefit below 0, E(bb<0).
A is preferred where there is little uncertainty of the positive benefits and costs of reversal are large.
DT is preferred when there is significant uncertainty, the opportunity costs of delay are small and data collection and analysis proceeds quickly.
The authors use the framework constructed above to analyze the prospect of adopting external cephalic version (ECV) treatment for pregnant women presenting in the breech position. ECV attempts to manipulate the fetus into a cephalic presentation and avoid a caesarian delivery.
This paper is interesting and possibly useful. The theoretical model is enlightening and warns decision makers to evaluate opportunity costs and reversal costs in addition to simply the direct cost of conducting a trial. The researchers also advise decision-makers when to choose which course of action in a comparative sense. Further, in the example using ECV, the authors actually mathematically calculate which course of action is preferred in this real-life situation. Much of the value of the framework, empirically, depends and having accurate information. Do decision-makers know how many people are affected by the disease? Are the cost of referrals known? Can we accurately estimate the value of information gained from a future trial? If the answer to these questions is ‘yes’, then this paper is very useful; if not, it is still a clever model.
Eckermann and Willan (2007) “Expected Value of Information and Decision Making in HTA,” Health Economics, vol 16, pp. 195-209.

TeraMedica launches its VisLite information manager

Milwaukee-based medical informatics company TeraMedica announced the launch of its new Evercore - VisLite Information Manager.
The system is part of the company’s effort to address interoperability challenges in the Visible Light arena. “VisLite integrates and manages ophthalmology, surgery, endoscopy, pathology, microscopy and many other areas,” said Jim Prekop, CEO and president. "Evercore - VisLite Information Manager integrates and manages all visible light modalities, into a single cohesive infrastructure.”
The information manager then serves the visible light dataset to a facility’s electronic medical record (EMR) or RHIO/electronic health record (EHR). “The integrated clinical focus includes connectivity of visible light data sets with traditional medical images and MPEG2, JPEG, BMP files, in a patient centric context. The aggregated device information is gathered seamlessly from systems such as the fyreLINK VL Image Capture system,” added Prekop.
Evercore - VisLite Information Manager also uses open standards in managing and storing digital information and makes the visible light data available at the point of patient care across the health care enterprise in multiple formats and at multiple service levels. “Evercore can literally disappear into a facility's infrastructure, allowing clinicians to focus on patient care instead of on information retrieval,” said Prekop.

Kaiser exec resigns amid EMR-initiative controversy

The executive overseeing Kaiser Permanente’s ambitious $3 billion electronic medical records initiative resigned Tuesday, according to an article in the Los Angeles Times. J. Clifford Dodd, senior vice president and CIO, quit four days after another employee, Justen Deal, sent out an email saying that Kaiser was wasting money and should scrap the project – known as HealthConnect – for “a system that can handle the scale of a company like Kaiser.”

Deal’s email said that problems with HealthConnect has resulted in cost overruns and software breakdowns affecting access to medical records. A Kaiser spokesman said the email was not the reason for Dodd’s resignation and that the allegations were untrue. The HealthConnect rollout has exceeded expectations, the spokesman said. Deal was put on administrative leave pending an investigation of whether he violated company e-mail policies.

Despite the push for paperless medical records, only 20 percent of physicians are using electronic medical record systems. As the nation’s largest nonprofit health organization, Kaiser Permanente’s high-profile digitization project has many in the industry keeping watch. Failure of such a large-scale project could cost Kaiser millions and, in turn, might cost members higher premiums.

In the final quarter of last year, Kaiser posted its first loss in three years, at $211 million. However, the company posted $417 million in third-quarter profit on $8.7 billion in revenue on Tuesday, roughly an 11 percent increase over the same period one year ago.