Information

You are currently browsing the archive for the Information category.

Consumer Satisfaction with Healthcare Websites

December 22, 2011 in Healthcare IT | No comments

ForeSee released a study showing which kinds of healthcare-oriented websites do the best job satisfying customers. Their results show health insurance websites have dismal customer satisfaction compared to other kinds of healthcare sites (such as pharmaceutical sites, hospital websites, health information sites, etc.). A summary of the overall customer satisfaction rates are below.

  • Health Information Websites: 78
    • Public (federal government and nonprofit): 78
    • Private :79
    • Pharmaceuticals: 76
    • Products: 76
  • Hospital and Health System Websites :78
  • Health Insurance Websites: 51

Tags: , , ,

Medicare Physician Payment Adjustments

November 21, 2011 in Healthcare IT, Medicare, P4P, Physician Compensation, Quality | No comments

The Medicare billing system is complex.  There an alphabet soup of acronyms, (e.g., RVUs, CPT, HCPCS, GPCI) and each of these affects payments in different ways.  In addition to the standard payment terms, Medicare is also creating additional payment incentives.  These payment incentives fall into three broad categories:

  • Quality reporting
  • e-Prescribing (eRx)
  • Electronic Health Records (EHR)

CMS’s Physician Quality Reporting System (PQRS) allows physicians to report the quality of care their patients receive. Physicians can report PQRS measures through claims, registries, or EHR systems.  To incentivize physician participation in the PQRS, CMS has adopted incentive payments.  In 2012-2014, Physicians who meet the PQRS participation requirements will receive a 0.5 percent payment bonus.  In 2015 through 2017, however, who do not submit a sufficient number of PQRS measures actually will receive a payment reduction.

In addition to the PQRS incentive, beginning 2012, Medicare eligible professionals who are not successful electronic prescribers under the eRx Incentive Program to a payment adjustment. This payment adjustment applies to all of the eligible professional’s Part B-covered professional services under the Medicare Physician Fee Schedule (MPFS). From 2012 through 2014, the payment adjustment will increase with each new reporting period. Accordingly, for 2012, eligible professionals receiving a payment adjustment will be paid 1.0% less than the Medicare Physician Fee Schedule (MPFS) amount for that service. In 2013 and 2014, the payment adjustment increases to 1.5% and 2.0% respectively.

A table summarizing these incentive payments is below.

Year PQRS eRx
Incentive Payment MOC Incentive Sucessful
2011 1.0% 0.5% 1% N/A
2012 0.5% 0.5% 1% -1%
2013 0.5% 0.5% 0.5% -0.5%
2014 0.5% 0.5% N/A -2%
2015 -1.5% N/A N/A N/A
2016 -2.0% N/A N/A N/A
2017 -2.0% N/A N/A N/A

CMS also offers physicians incentive payments to adopt EHR.  Incentive payments can be as high as $18,000 per year or $44,000 over a five year period.

Tags: , , , , , ,

Is your personal health data secure?

October 13, 2011 in Healthcare IT | No comments

In 2010, providers alone spent more than $88.6 billion on health IT initiatives in response to the US government’s “meaningful use” incentive program to drive widespread adoption of electronic health records (EHRs).  Is this data secure?

For many individuals, the answer is no.  In just the last year and a half, a breach of personal health information occurred, on average, every other day.  The HHS Office for Civil Rights even lists where and when these breaches occurred.

PWC lists four key factors that put your health data at risk.  These include:

  • Electronic Health Records (HER) and Health Information Exchanges (HIE)
  • Business Associates (e.g., vendors)
  • Use of health data as secondary data for clinical studies, outcome-based research, and post-market surveillance of drugs.
  • Social Media.

Quotations

“Our policy restricts employees and physicians from accessing their own medical records, but there have been cases where curiosity gets the best of them,” said Thomas J. Lewis, president and chief executive officer at Thomas Jefferson University Hospitals, Philadelphia.

“We need to meet the physician and patient needs and demands for mobile health and social media, but we are still focusing on how we manage the security implications. There is a direct correlation between the level of mobility and our ability to protect that data,” said Luis Taveras, senior vice president of information technology services at Hartford HealthCare Corp., an 867-bed major teaching facility in Connecticut.

 

Source

Tags: , , , ,

Kaiser Permanente’s EHR System

December 20, 2010 in Healthcare IT | No comments

As of 2009, only 9 percent of America’s hospitals were using even a basic form of electronic medical records (EHR) and as of 2008 only 13 percent of practicing doctors were doing so.  Yet one private health insurer has integrated EHR for hospitals, physicians, outpatient and other services.  I am of course talking about Kaiser Permanente.

Today I will review the book Connected for Health, which details how Kaiser implemented EHR in their system.  The book is not an objective evaluation in that it is written by the people who participated in Kaiser EHR implementation.  The lack of objectivity, however, is more than offset by the “insider” point of view the authors offer.  This is not a book for people interested in a fun read or general health policy.  However, if you are interested in implementing EHR in your organization, this book will likely prove invaluable.

Kaiser Overview

Kaiser Permanente is an enormous organization. It employs 14,000 physicians, 45,000 nurses, and thousands of other clinicians and staff.  It has nine regions: Northern California, Southern California, Colorado (Denver), Colorado (Southern), Georgia, Hawaii, DC/Maryland/Virginia, Ohio, Oregon/Washington.

Kaiser EHR Functionality

Kaiser’s electronic health records system, KP HealthConnect, is based primary on software from Epic Systems of Wisconsin. The KPHealthConect system has the following functionalities:

  • A personal health record,
  • Outpatient practice management
  • Outpatient clinicals (e.g., physician order entry, clinical documentation),
  • Inpatient billing,
  • Inpatient pharmacy,
  • Inpatient administrative systems,
  • Inpatient clinicals,
  • Non-Epic Systems and Pre-existing applications that integrate with KPHealth Connect.

Of particular interest is the personal health record.  Patients can view most parts of their medical record such as lab results, immunizations, past office visits, prescriptions, and more.  Patients can send secure messages to providers and view, schedule or cancel appointments.  Members can also view information on health risk assessments, drug encyclopedias, and use health insurance management tools.

Read the rest of this entry »

Tags: ,

EMR and Second Opinions

September 16, 2010 in Healthcare IT | No comments

Electronic medical records have been touted as producing large gains in efficiency.  In fact, Kaiser Permanente has invested $3 billion in EMR.  One drawback of EMR, however, is that the value of second opinions may fall.  Instead of coming to a new physician with a clean slate or at the least seeking a fresh interpretation of a test, ‘second-opinion physicians’ in the same physician network will have access to the original physician’s diagnosis.  This may bias the doctor towards agreeing with the original interpretation of the patient’s condition.

To what degree do ‘second-opinion physicians’ currently have access to the diagnosis of the referring physician?  If they already have access to the original doctors notes or frequently call this physician to consult with them, the advent of EMR may not change the bias of the second-opinion physicians.  In fact, access to better information may improve the quality of second opinions.  On the other hand, if the increased amount of information available to the second physician changes their thought process, then they may be more likely to agree with the original physician.

Is this one of the cases where more information is actually a bad thing?

Tags: ,

Risk Adjustment Tools for Measuring Physician Quality

November 12, 2009 in Information, Physician Compensation, Quality | No comments

A paper by Thomas, Grazier, and Ward (2004) analyzes a variety of risk adjustment software products. Using these six risk adjustment products to calculate physician efficiency scores, they found “moderate to high levels of agreement were observed among the six risk-adjusted measures of practice efficiency.” However:

“And even though our analyses suggest that 50 percent to 60 percent of adult PCPs identified by their system as being high outliers are likely to be identified by other profiling systems as well, the client has no way to know which of the identified outliers are the ones that multiple systems would agree on. Thus the profiling client must deal with practice efficiency rankings knowing that, in all likelihood, 40 percent to 50 percent of PCPs identified as high outliers are actually not among the least efficient 10 percent of primary care physicians.”

The authors also compare two quality score metrics. The first is the ratio of the physician’s observed cost with the expected cost based on the physician’s patient’s risk scores. The O/E score is equal to:

  • (O/E)k=yk/Yk

Above, yk is physician k‘s observed score and Yk is their estimated score. The authors believe, however that the O/E score is not ideal. It is biased against providers who have a small sample size of patients. Thus, physician’s with smaller patient panels in the data set are more likely to be considered outliers. On the other hand, the authors advocate using a standardized cost difference (SCD). The SCD is calculated as follows:

  • SCDk=(yk-Yk)/[σ/(Nk)1/2]

The SCD measure explicitly takes into account the physician’s sample size. A large sample size will move the SCD more towards the difference in observed and expected costs; a small sample size will move the SCD score closer to the mean of 0.

Below is a list of the six risk adjustment tools used in the paper:

  • Adjusted Clinical Groups from Johns Hopkins University. Adjusted clinical groups cluster health plan members having similar comorbidities into groups that have similar resource requirements and clinical characteristics. The ACG Case-Mix System then uses a branching algorithm to place each patient into one of 82 discrete, mutually exclusive categories based on the mix of clinical groups experienced during the time period under study.
  • Burden of Illness Score from MEDecision, Inc. This system is based on MEDecision’s Practice Review System (PRS), which partitions care into episodes of illness and assigns services, severity levels, and medications to these episodes. The BOI Score is a linear-scaled measure that indicates relative health care cost risks associated with the particular mix of episodes experienced by a patient during a defined time period.
  • Clinical Complexity Index from Solucient, Inc. The CCI methodology considers age, severity, comorbidity, hospital admissions, and categories of diagnoses (acute, chronic, mental health, and pregnancy) to assign patients into mutually exclusive CCI risk categories. Although the system provides for 1,418 different categories, 95 percent of patients fall into just 45 of these.
  • Diagnostic Cost Groups from DxCG, Inc. The DCGsystem includes a whole family of multiple linear regression models.
  • Episode Risk Groups from Symmetry Health Systems,Inc. Like BOI Score, ERGs are episode-based. The episodes underlying ERGs are created using Symmetry’s Episode Treatment Groups (ETGt) methodology, a basic illness classification system that uses a series of clinical and statistical algorithms to combine related services into more than 600 mutually exclusive and exhaustive categories. For a given patient, episodes experienced during a time period are mapped into 119 Episode Risk Groups, and then a risk score is determined based on age, gender, and mix of ERGs. For our analyses, we used the ERG retrospective risk score.
  • General Diagnostic Groups from Allegiance LLC. General Diagnostic Groups were developed using the Agency for Health Care Policy and Research’s Clinical Classification Software (CCS). CCS aggregates individual ICD-9-CM codes identified on health care claims into 260 broad diagnosis categories for statistical analysis and reporting. The GDG system then lumps together CCS categories considered to be clinically similar and to have similar associated per-patient charges into 57 diagnostic categories. These 57 diagnostic categories are used as dummy variables in a multiple regression model for predicting health care costs.

Source:

Tags: , ,

Who would trust Wikipedia for medical information?

August 3, 2009 in Healthcare IT | 3 comments

The New Scientist reports that “Sixty-one per cent of American adults seek out health advice online.”  Looking for medical advice online is okay as long as you don’t rely on a unreliable sites.  For instance, who would trust a user generated site like Wikipedia?  The answer to this question, is doctors.

According to a report in April by US healthcare consultancy Manhattan Research, fifty percent of U.S. doctors turn to Wikipedia for medical information.

Tags:

Value-Based Cost Sharing

February 4, 2009 in Asymmetric Information, Health Insurance, Optimal Ins (Theory) | No comments

Many policy experts have been proponents of value-based cost sharing.  Under value-based cost sharing, medical care that is seen to provide a higher marginal benefit to the patient will have lower coinsurance rates than medical care with lower marginal benefits.  If value-based cost sharing would be implemented, preventive care should have low coinsurance rates because of the subsequent health benefit.

This seems like a very attractive way to design health insurance benefits, but a paper by Pauly and Blavin (JHE 2008) asks if value-based cost sharing is truly a novel concept.

First, under perfect information, there is no reason to have value-based cost sharing.  In this case, patients should internalize the marginal benefits–now and in the future.  ”When all agents have perfect knowledge of patient illness states and benefits of care, optimal coinsurance should be zero; insurance should take the form of a fixed dollar (indemnity) payment to cover the full cost of care when care is cost-effective, and should pay nothing in circumstances in which care has benefits that fall short of cost.”

Under asymmetric information, however, patients may make naive decisions.  For instance, patients may decide to forgo preventive care because the do not realize its long-term health benefits.  Coinsurance rates must still be designed to balance the twin goals of risk sharing and averting moral hazard.  Lower coninsurance rates will incentivize patients to get needed care.  However, designing coinsurance rates solely based on the marginal benefit of the procedure may not be optimal once we take into account that patient moral hazard may increase medical care above optimal levels.  

Pauly and Blavin also note that under asymmetric information, “it may now be the more price responsive service that should get the cost reduction, since lowering its cost sharing will have a larger effect in terms of moving it closer to the ideal level than would be the case for a less responsively demanded service.  That is, if patient ignorance resulting in underestimation of benefits from care in a given setting is severe enough, the direct relationship between price responsiveness and optimal cost sharing should be reversed.”

Yet just because value-based cost sharing can work does not mean that is the only solution.  Instead of spending money to reduce coinsurance rates, insurance companies or policymakers could spend money to inform consumers of the true benefits and costs of different types of medical care.  This is especially true of medical services that are not price responsive; where the only way to convince patients to undergo these potentially uncomfortable procedures is to increase information dissemination.  For instance, most people would prefer not to undergo a colonoscopy even at a price of 0.  The only way to convince patients that the procedure is needed is by information dissemination.

Value-based cost sharing is not a magic bullet, but may be a useful technique in the presence of asymmetric information.

Tags: , , , ,

Learning and Drug Diffusion

February 2, 2009 in Asymmetric Information, Pharmaceuticals, Supply of Medical Services | 1 comment

How do doctors know which drugs to give to which patients?  Of course there are clinical trials giving the relative efficacy of each drug.  With less than perfect adherence, however, clinical trials may not accurately predict a drug’s efficacy or the potential side effects.

A paper by  Chintadunta, Jiang and Jin (2008) look at two types of physician learning about pharmaceuticals: learning across patients and learning within patients.  Across patient learning occurs when the physician learns about the average quality of a drug.  Physicians figure out which drugs are best suited for individual patients through within-patient learning.

The authors look at physician learning in the setting of Cox-2 Inhibitors:

“Between 1998 and 2001, the FDA approved three Cyclooxygenase-2 (Cox-2) Inhibitors: Celebrex (Dec. 1998), Vioxx (May. 1999), and Bextra (Nov. 2001). All of them were heavily advertised as safer alternatives to then existing pain killers. By September 2004, the class had more than 10 million patients, annual sales had reached $6 billion in 2003, and total advertising dollars spent in 2003 were as high as $400 million. After a clinical trial associated Vioxx with severe cardiovascular (CV) risks, Merck withdrew the blockbuster drug in September 2004. CV risks and enhanced concerns on skin irritation led to the withdrawal of Bextra in April 2005. As of today, Celebrex is the only Cox-2 Inhibitor remaining on the market, with warnings added in April 2005. ”

Data and Methods

The authors use data from four sources to identify physician learning: 

  1. patient-level prescription and satisfaction data from the IPSOS patient diary database (IPSOS-PD), 
  2. monthly advertising expenditures obtained from the New Product Spectra (NPS) database, 
  3. the number of news articles covering Cox-2s derived from Lexis-Nexis for the period 1999 to 2005, and
  4. the number of academic articles covering Cox-2s from Medline from 1999 to 2005. 

Patient level satisfaction is importance because the more satisfied a patient is with the prescription, the less likely the physician will decide to switch brands.  Physician learns which brands are well-suited to which type of patients.  Advertising, media coverage and academic articles all play a roll in across-patient learning.  

The authors use a Bayesian model to identify learning.  The authors assume each physician has a prior about the relative efficacy and safety of each of the Cox-2 Inhibitors.  Patient satisfaction, advertising, media coverage, and academic articles all will update the posterior probabilities.  

The utility of each of patient, p, from using each drug, j, at time, t, is:

  • Upjt = β0j + βsE(satis)jt + βxjXpt + βzZjt +εpjt

The patient’s utility depends on a drug specific constant, the patient’s average satisfaction rating up to that date, patient specific factors, X, and drug specific information, Z.

Results

The authors find that there is significant physician learning based on the evolution of patient satisfaction measures.  Other types of information seem to have less of an effect on physician prescribing behavior.  Physicians hold strong priors which leads to slow updating.

Interestingly, “News articles have a positive influence on prescriptions, no matter whether these titles sound negative or non-negative. This suggests that the major role of news articles is informing doctors/patients of the existence of Cox-2s, rather than revealing the quality of Cox-2s…In contrast, a medical article about Cox-2s has a significant negative impact on prescription sales, even if its title and abstract are non-negative.”

Thus, the authors find that doctors learn more within-patient than across patient.  

FDA updates have no impact on physician behavior.  How can this be?  By the time the FDA issues a warning on the risks of the potential risk of different drugs, there almost always have been academic articles published on these issues.  This is not to say that the FDA updates are useless, only that they often come after the academic research has already taken place and the physicians have already updated their priors.

For policy makers, the conclusions of this study may be worrisome for proponents of evidenced based medicine.  Currently, physician learning from new information is very slow.  Secondly, physicians seem to focus on tailoring treatments to individual patients rather than updating whether or not the treatment is worthwhile in the first place. 

Tags: , ,

Optimal Contracts in the British NHS

January 20, 2009 in Asymmetric Information, Hospitals, International Health Care Systems, Supply of Medical Services | No comments

One of the perennial questions of interest for health services researchers how to pay for health care.  A paper by Chalkley and McVicar (2008) examines this question in the contest of a reform in Britain’s National Health Service (NHS).

“After 1990 hospitals, which had previously been under the direct control of Health Authorities, could apply for NHS Trust status whereby they would be given discretion over employment, remuneration scales and the disposal of assets. This discretion was subject to limits and reservations and could ultimately be revoked if the appropriate authority, in this case the Secretary of State for Health, deemed their actions to be against the ‘public interest’.”

There are three types of payment contracts between the NHS and health authorities.  The first is a block contract where hospitals receive a flat contract to care for a patient population regardless of the actual care given.  The second contract is a cost-per-case contract where the hospital is paid based on the cost of the medical services supplied.  Volume contracts similarly base payment on the quantity of care provided.  The final contract is a sophisticated block contract.   This is similar to the simple block contract but requires the NHS to monitor the hosptials to ensure that they are providing the required care.

The authors offer 4 conjectures of when different contracts will be adopted.  When contracts will be adopted depends on 3 characteristics: variability in cost, variability in volume and easy of observing patient treatment.

  1. When variability in cost and volume is small or variability of cost is small and volume is easily observable]then block contracts will be favored.
  2. When monitoring costs are low, variability of volume is large, variability of cost is small then sophisticated block contracts will be favored.
  3. When variability in cost is large and variability of demand is large then cost-per-case payments will be favored.
  4. Characteristics of purchasers and/or providers that mitigate monitoring costs will give rise to a greater use of volume-dependent and sophisticated block contracts relative to simple block contracts. 

The results from a multinomial logit regression support these conjectures.  

Thus, although policymakers would prefer a one-size-fits all contractual arrangement, the authors show that contractual arrangements between the health purchaser and the provider must take into account the variability inherent the good being supplied.  For instance, simpler contracts should be favored when monitoring costs are high, but more complex contracts may be feasible with lower contractual cost.  Low variation in cost and volume makes block payments very attractive.  However, when there is significant variation in cost or volume, cost-per-case or volume-dependent contracts can help mitigate the providers financial risk.

Tags: , ,

« Older entries