 To determine the frequency of and reasons for medically unnecessary hospital days.

 We developed an online survey to pro-spectively collect data on hospitalists' and discharge planners' perceived delays in treatment or discharge for patients on their general medicine services. Over a 2-month period, hospitalists and discharge planners completed a daily online survey. 

 We collected data on 3574 patient-days from our hospitalists and data on 2502 patient days from our discharge planners. Among the hospitalists'responses, 395 patient-days (11%) were thought to be unnecessary. Among the discharge planners' responses, only 186 patient-days (7%) were thought to be unnecessary. The hospitalists believed that the most common reason for discharge delay was lack of extended care facility availability (111 patient-days [28%]), followed by patient or family reasons (62 patient-days [15%]), procedure delays (62 patient-days [15%]), and test scheduling delays (52 patient-days [13%]). The discharge planners' data were similar.

 More than 10% of hospital days were reported by our hospitalists to be unnecessary at this academic medical center. Major reasons were lack of extended care facility availability, patient or family reasons, procedure delays, and test scheduling delays. A simple survey instrument to assess perceived delays in the hospital may provide real-time information to initiate improvement changes to reduce excess hospitalization days.

	More than 10% of hospital days were reported to be unnecessary at this academic medical center.

		This figure is in alignment with previously reported studies.

		Hospitalists working independently or with house officers reported similar rates of excess hospitalization days. 

		Because hospitals are employing more hospitalists, the use of a simple real-time data collection tool to identify the frequency of and reasons for excess hospitalization days may focus improvement efforts in these highly occupied organizations.

	Excess hospitalization days are of major concern to patients, payers, hospitals, and healthcare providers. Implications of excessive length of stay include the following: (1) greater costs, (2) potentially increased iatrogenic illness, and (3) high hospital occupancy with resultant loss of efficiency and access. To address these concerns, hospitals have developed support systems that include discharge planning services, home care services, and hospitalist services.

	According to the Society of Hospital Medicine,

 the hospitalist field has grown rapidly, with more than 30,000 hospitalists now in practice. At academic medical centers (AMCs) in particular, many programs have expanded to include the following 2 types of hospitalist services: (1) the traditional resident team (RT), where a hospitalist is the supervising attending physician, and (2) a non—house staff hospitalist team (HT), where the hospitalist works independently to provide direct patient care.

 The growth of hospitalist programs in AMCs has been largely attributed to (1) offloading the volume of patients on RTs limited by work hour rules of the Accreditation Council on Graduate Medical Education and (2) filling an overall increasing demand for hospital care services.

 published a study on the epidemiology of unnecessary hospital days in an AMC. Subsequent studies

 have measured and classified excess hospitalization days to assess the potential effect on healthcare systems. These studies found 10% to 20% of hospital days to be unnecessary. To determine the frequency of and most common reasons for delays on the general medicine services at our AMC, we asked our hospitalists and our discharge planners to complete a daily online survey. To our knowledge, this is the first study comparing the views of hospitalists (on RT and HT services) and of discharge planners on excess hospitalization days.

	Our hospital is a tertiary care AMC with more than 600 adult beds and 34,000 annual discharges. The 39 hospitalists in our group staff almost 10,000 total discharges. Hospitalists serve as supervising attending physicians on RTs and see patients individually on HTs. Both teams provide hospital coverage 24 hours a day and 7 days a week. Each discharge planner works with 1 to 3 of these RT or HT services.

as guides, we developed a brief (<5 minute) online survey instrument that asked hospitalists and discharge planners whether any of their patients on the general medicine services experienced an excess hospitalization day. They could further categorize the delay as an intrahospital delay (related to an inhospital service not completed in a timely manner) or as a discharge day delay (prevented discharge of a patient who was otherwise medically ready for discharge). To facilitate completion of the voluntary surveys, we generalized the comprehensive categories of delay from previous studies to 4 intrahospital delay choices and 5 discharge day delay choices. The  respondents were restricted to a single response choice and could type in more detailed information as free text, or they could select “other” and write in a response. We identified  the hospitalists and their respective discharge planners working on the day shift for each day of the survey from a prearranged work schedule and sent daily e-mail messages asking them to independently complete the surveys from August 6 to October 1, 2008. The survey is given in the 

	The survey responses were the only data source utilized in analyzing our results. Descriptive statistics were used to assess the frequencies and categories of excess hospitalization days. 

2 Analyses were used to assess unadjusted bivariate comparisons between groups of interest with respect to reasons for the excess hospitalization days. Two-tailed a was set at .05. All analyses were performed using commercially available statistical software (STATA/IC 10.0; StataCorp LP, College Station, Texas).

	Hospitalists completed 65% of the potential total number of surveys, yielding data on 3574 patient-days, and discharge planners completed 63% of the potential total number of surveys, yielding data on 2502 patient-days. The hospitalists’ data included 1632 patient-days from the RT services and 1942 patient-days from the HT services. Among all hospitalist’ responses, 395 patient-days (11%) were believed to be unnecessary. The RT patients experienced 171 delay days (11%), while the HT patients experienced 224 delay days (12%). Further comparison between weekdays vs weekend days between the RT and HT services revealed no significant differences. Among all discharge planners’ responses, 186 patient-days (7%) were believed to be unnecessary, a statistically significant difference compared with the hospitalists’ responses (P <.01).

	Of 395 excess hospitalization days reported by our hospitalists, 224 (57%) were due to discharge day delays, and 171 (43%) were due to intrahospital delays. The most common reasons for excess hospitalization days were lack of extended care facility availability, patient or family reasons, procedure delays, and test scheduling delays. 

 lists the most commonly selected categories of delay as reported by our hospitalists and our discharge planners.

	Of 186 excess hospitalization days reported by our discharge planners, 145 (78%) were due to discharge day delays, and 41 (22%) were due to intrahospital delays. The discharge planners generally agreed with the hospitalists’ categorization of the most common reasons for excess hospitalization days (Table 1).

 Specific Categories of Intrahospital Delays

	As part of a postsurvey analysis of intrahospital delays, 2 hospitalist reviewers (ALH and RFP) manually reviewed each free-text response provided and totaled the number of days attributed to each type of delay (

). From the hospitalists’ perspectives, procedure delays accounted for most of the intrahospital delays, followed by consult delays. Among the procedure delays, radiology-related services accounted for more than half of the delay days. The discharge planners provided fewer specific reasons for delays but generally agreed with the hospitalists’ views on intrahospital delays.

	Hospitalists at our AMC perceived 11% excess hospitalization days, with no significant difference between the RT and HT services. Our discharge planners perceived 7% excess hospitalization days. Both hospitalists and discharge planners identified the following categories of discharge delay to be the most common: (1) lack of extended care facility availability, (2) patient or family reasons, (3) procedure delays, and (4) test scheduling delays.

	We note similar rates of excess hospitalization days across study locations and periods relative to previously published studies. Carey et al

 reported a 14% rate of unnecessary hospital days in their general medicine services over 2762 days, while Selker et al

 reported a 17% rate of unnecessary hospital days among general medicine and gastrointestinal patients over 7795 days. More recently, a study

 of hospitalized general pediatric patients identified 9% excess hospitalization days over 911 days.

	The hospitalist movement represents a dramatic evolution in healthcare, with 30,000 hospitalists now in practice.

 At most AMCs, where the house staff census remains fixed and the demand for hospital care increases, medicine hospitalist groups have expanded their presence to provide direct patient care.

 Resident teams have maintained the traditional structure of attending, resident, interns, and students. Attending-only hospitalist teams can theoretically run “leaner,” with improvements in efficiencies and work flow. However, our study shows no difference in the number of excess hospitalization days between these 2 types of general medicine services. When the categories of delays were classified as intrahospital delays vs discharge day delays, the rates were again similar between the 2 types of hospitalist services (38% on RTs and 47% on HTs for intrahospital delays vs 62% on RTs and 53% on HTs for discharge day delays). Despite speculation that direct attending—level care by hospitalist teams could run more efficiently and result in fewer excess hospitalization days, further studies are needed to determine how hospitalists’ daily work flow compares with that of house staff–run teams and whether these differences lead to greater efficiencies in patient care services. It may be that the lack of differences observed between the 2 types of hospitalist services in our facility reflects external factors beyond the control of primary hospitalists. As hospitals have engaged discharge planners, home care services, and hospitalists, the next logical approach may be to identify and work with specific hospital services such as stress tests, radiology studies, and gastrointestinal procedures, as well as to employ skilled nursing facilities to establish partnerships to expedite the transition of patients from the acute care facility. Providing real-time feedback to hospital administration and providers regarding specific reasons for excess hospitalization days may drive health system efficiency efforts to these selected areas identified by the hospitalists.

	Our analysis also revealed that among the top perceived reasons for excess hospitalization days was patient or family unreadiness. For many patients, the course of a hospital stay can be an intense experience and require ongoing care as they transition out of the acute care setting. Early discharge initiatives can prepare patients and families for the discharge process.

 Patients and their family members need to be engaged and actively involved in the discharge process, identifying needs and facilities even as early as at the time of admission.

	Excess hospitalization days raise concerns for patient safety, quality, and efficiency, but also herald significant financial costs to the healthcare system as a whole. Extrapolating our data over a year’s time yields 5500 excess hospitalization days (11% of 10,000 discharges staffed, with a mean length of stay of 5 days). Patients and healthcare practitioners and administrators should certainly appreciate the resultant cost burden. These resources could be better used to provide care to a broader patient population within the community.

	Our study also included the perspectives of discharge planners, who work closely with our hospitalists, and found that the discharge planners perceived a lower rate of excess hospitalization days. Closer evaluation of the survey responses revealed that our discharge planners perceived that a greater percentage of delays occurred after patients were  believed to be medically ready for discharge than our hospitalists (and vice versa, where the discharge planners thought that fewer delays were attributable to intrahospital delays compared with our hospitalists). Although we can only speculate as to the reasons for these differences in perception, we believe that contributing factors to this observation may include the following: (1) discharge planners, whose specialty is to primarily focus on the needs of patients after (or near the point where) they have been deemed ready for discharge, may have been biased to find more delays in this category, and (2) discharge planners in general do not review all the consultant records, nor do they speak with the diagnostic or therapeutic procedural departments to order tests or obtain results. Therefore, they may not always have had the information to determine if there was an intrahospital delay and could have underestimated this category of delays.

	Ongoing quality improvement initiatives to reduce the number of excess hospitalization days are proposed. A simple data collection tool can help identify the most common reasons for excess hospitalization days on a real-time basis, which in turn can be used to deploy countermeasures with appropriate hospital-based specialists and staff. In our organization, we expect to further explore the intrahospital delays as characterized in Table 2. By implicating procedural services with the largest proportion of excess hospitalization days,  valuable feedback can be provided to these departments to improve patient flow. Furthermore, although our data did not directly evaluate whether weekend services led to greater rates of excess hospitalization days, the literature suggests that weekend service restrictions may contribute to inappropriate delays and that weekend availability of services should continue to be monitored for perceived delays.

This type of real-time information regarding specific hospital services may be helpful to hospital administration focusing on efficiency of clinical operations.

	There are inherent limitations to our quality improvement survey. First, our data were collected from a single AMC, and the results may not be generalizable. Furthermore, given that our study population included patients admitted to the general medicine services, the results may not be applicable to other clinical services such as pediatrics and surgery. Second, we had less than 100% compliance with completion of the survey. In particular, if there was some sort of systematic lack of response in which some hospitalists may have responded to the survey at a much lower rate than other hospitalists, there may be overrepresentation or underrepresentation biases introduced within the results. Because we collected data on more than 3500 patient-days from our hospitalists and more than 2500 patient-days from our discharge planners, we believe that the data are fairly representative of our AMC during the study period. Third, we identified a difference in the perception of excess hospitalization days between the hospitalists vs the discharge planners. This could be attributed to our survey process in which discharge planners did not complete surveys on weekends, and review of the data showed that some discharge planners reported an unspecified number of multiple delay days. Given the lack of clarity, we input these data points as a single delay day, which may have underestimated some of the discharge planners’ perceptions. Fourth, weekend days may be times of reduced efficiency, with at least 1 study

 noting resultant excess hospitalization days on weekends, and there are likely other forces (such as staffing and hours of operation) that can lead to excess hospitalization days for a hospital. Fifth, given that our survey allowed for respondents to select only a single response, it is unclear how respondents may have dealt with competing reasons for a discharge delay, which in turn may have affected some of our categorization of results. Furthermore, because the primary intent of our evaluation was to determine the frequency of delay, we did not ask our discharge planners or our hospitalists to include patient-specific information, and we were unable to perform any sort of interrater comparative statistical analysis.

	In conclusion, more than 10% of hospital days on the general medicine services were found to be unnecessary at our AMC. A simple-to-use data collection survey can help staff recognize excessive hospitalization days in real time and quickly identify the reasons for delay. Hospitalists could work cooperatively with discharge planners to gather data prospectively and report their findings on causes of delay. This may be relevant to hospitalist groups, who care for an increasing proportion of inpatients. Moreover, such data should be of interest to hospital administrators, as well as to payers of this nation’s increasingly costly inpatient care.

Articles

WEB EXCLUSIVE

We assessed the frequency of and reasons for medically unnecessary hospital days, which affect patients, payers, hospitals, and healthcare providers.

Excess Hospitalization Days in an Academic Medical Center: Perceptions of Hospitalists and Discharge Planners

	Health insurers in India currently face many challenges, including poor consumer awareness, strict regulations, and inefficient business practices. They operate under a combination of stifling administrative costs and high medical expense ratios which have ensured that insurers operate under steep losses. External factors (eg, onerous regulations, lack of standards, high claims payouts) and internal factors (eg, high administrative costs, dependence on indemnity models that cover inpatient treatment costs only) have forced the health insurance industry into a regressive spiral. To overcome these challenges, health insurers need to innovate in their product offerings and tighten their existing processes and cost structures. But as a long-term strategy, it is imperative that health insurers deploy managed care concepts, which will go a long way toward addressing the systemic issues in the current operational models of health plans.

	Managed care firms can play a key role in India's emerging health insurance sector and help strengthen the country's overall healthcare effectiveness.

		The health insurance industry in India today is loss making and does not use any managed care techniques.

		Simple and innovative managed care techniques can go a long way toward addressing many of the industry challenges.

		Because there is a need for expertise on policy formulation at the industry level, US managed care firms have the opportunity to play a key role in this emerging sector.

India offers a wide variety of healthcare services to its population. On one hand there are the advanced hospitals and diagnostic centers in urban areas, whereas the rural areas depend significantly on government health centers. Between these 2 extremes there are government hospitals, private hospitals, private practitioners, dispensaries, and clinics (including Indian traditional medicine systems). Despite health being a major government concern, the majority of India’s health infrastructure is in the private sector and more than 70% of healthcare expenses are met by consumers and not the government (

). Given this unbalanced mix of healthcare funding and a per capita income of less than $600, medical costs are unaffordable for a majority of India’s population.

To address healthcare affordability, commercial health insurance was introduced in India by the government-owned general insurers as a standardized annual indemnity product in the mid-1980s, with a view to providing alternate financing options to members and helping them access better quality healthcare. Today, with the increased liberalization of the insurance industry, many private players have entered the health insurance market, resulting in increased awareness and growth of commercial health insurance (

).  Currently, around 20% of the total population is covered under health insurance schemes, with the majority covered under either government or employer programs; regular commercial health insurance has less than 2% penetration.

CHARACTERISTICS OF HEALTH INSURANCE IN INDIA

Despite the liberalized environment and increased presence of private insurers, the health insurance industry has been characterized by high government control and lack of innovation, which has stunted the overall growth. The sector has not evolved significantly enough to play a major role in the country’s health policy. Some of the systemic characteristics of the industry are listed here.

Health insurance is governed by the Insurance Regulatory Development Authority of India, which is the apex body for regulating all insurance firms (life, property, casualty, and health). Currently, health insurance schemes are mostly provided by life insurance and non—life insurance companies. Only 3 firms have recently started business as stand-alone health insurers. Hence most of the regulations covering health insurance flow down from the regulations for the life insurance and non–life insurance sectors, which include an approximately $25 million capital requirement to start a health insurance business. Moreover, current industry regulations prevent stand-alone health insurers from tying up with banks as a distribution channel. In India, banks have the maximum reach in terms of branches and geographical coverage, and are the primary channel for distributing all financial and insurance products.

 It is hence imperative that health insurance industry regulations get calibrated to reflect the business challenges.

Health insurance in India is sponsored by both government and private insurers. It was first marketed by the governmentowned insurers as a standardized annual indemnity product in the mid-1980s, and the premiums were largely based on the annual limit chosen and the age of the prospect. Today, more than 40 insurance firms (including life, general, and standalone health insurers) provide health insurance products.

One of the biggest drawbacks is the lack of standard terminology and protocol in treatment and billing of common illnesses. In many instances, different hospitals across the country use different terms and follow different treatment protocols and charges for treating the same medical condition.

 Additionally, hospitals provide diagnosis and treatment details in their claim forms in descriptive form (due to poor awareness of medical coding standards), which further complicates the claims analysis and processing for the insurers.

Health plans introduced in India in the mid-1980s by the government insurers were standardized annual indemnity products covering only inpatient hospitalization. After the industry liberalization in the early 2000s, private insurance firms started to offer health insurance products. Despite the presence of many players, indemnity plans covering inpatient treatment costs continue to be the form that dominates.

Prior to the industry liberalization, health insurance products were provided only by the government insurers, and they depended on unorganized intermediaries to help sell and administer these health products. In order to regulate these intermediaries, the Insurance Regulatory and Development Authority Regulations Act (2001) formalized the role and regulations of these healthcare intermediaries and renamed them Third Party Administrators (TPAs). Although the original role of a TPA was to provide the back-office administrative services to insurance companies, most TPAs today also deal with provider networks and claims payments. Currently there are more than 25 TPAs in operation, processing more than 2.4 million claims annually.

 For these services, the health insurers pay around 5% of the premium collected as fees to the TPAs.

Health insurance products carry a high administrative cost component. Without in-house health expertise, the industry depends on agents and TPAs to market and administer the health business by paying high fees. Hence, adminn istrative expenses are more than 35%, inclusive of agent fees of around 5%, TPA fees of 5%, and insurer’s operational expenses of 20%.

Since the parent insurance firms are focused on other sectors (such as life and property), the firms have no incentive to innovate on health products and prefer to subsidize their health products, resulting in unprofitable operations. With poor product innovation and misaligned pricing and benefit design, the medical expense ratio (also known as the incurred claims ratio) runs at more than 100% of the premium collected for most insurers.

Despite the robust growth in health insurance and increased awareness among consumers, the health insurance industry is not profitable. The combination of high administrative costs and high medical expense ratios has ensured that health insurers continue to have steep losses (

A quick analysis of the above aspects reveals that the industry operates in an environment which is not sustainable in the current form. The external factors (eg, onerous regulations, lack of standards, high claims payouts) and internal factors (eg, high administrative costs, dependence on indemnity models) have forced the health insurance industry into a regressive spiral. In the face of the above challenges, health insurers will need to change their business models to ensure ongoing and sustainable operations.

The expeditious approach for health insurers would be to innovate within the existing operational framework by introducing new products and streamlining the processes to bring

about more efficiency in their operations.

Currently, health products cover only inpatient treatment. Due to the nature of the benefit coverage, the insurer is brought into the care context only at the critical or inpatient treatment phase. The insurer neither has prior insight into the care pathway nor is updated on the earlier treatment stages and protocols. Health insurers have to innovate by offering benefits that cover outpatient, drug, and wellness (preventive care) programs. By expanding the benefits, insurers will get more insight and flexibility into managing the care pathways and can focus more on preventive care and reduce avoidable hospitalization.

Health insurers need to reassess the indemnity-dominated model by introducing plans that incorporate better risk sharing between stakeholders. Given that consumers in India tend to pay a higher out-of-pocket component, firms have to focus on products that combine indemnity with member liability such as copays, coinsurance, and deductibles. Also, given that India is traditionally a savings-led economy, health insurers have to create products with a strong savings and investment component. For example, in the life insurance sector (

), more than 90% of the policies sold have a savings or investment objective (such as money-back, profit-sharing, equity-linked, and mutual fund investment products).

Health insurers also need to widen the target pool of consumers. Currently, almost all health products are targeted at the middle-class segment of the population, with typical annual premiums of around $100 per member for an indemnity with annual maximum cover of $10,000.

 When compared with a mature health insurance market (such as that in the United States), the premium rates in India do not align with the overall health economics of the country (

). For example, the average premium in India is twice the per capita spending on healthcare, whereas in the United States the premium is half the per capita spending on healthcare. This kind of pricing has made health insurance unaffordable for the majority of the population and has resulted in products having limited market acceptance. A more careful assessment of the market and a more realistic pricing of the product, in alignment with the acceptable price points, will ensure wider acceptance of health insurance.

The industry needs to be proactive in controlling administrative costs. Some recent developments seem to suggest that the major players in the industry have recognized this need and have identified key measures for managing administrative costs. One of the recent trends has been to bring TPA services in-house with the objective of saving the TPA fees and providing better customer service.

The above approaches primarily impact the marketing and administration aspects of health insurance and are limited in their effectiveness in addressing the broader challenges in the industry, such as the high medical expense ratios. Hence, health insurers have to evaluate managed care techniques to help bring wider and sustainable improvements to the overall healthcare value chain.

NEED FOR APPLYING MANAGED CARE TECHNIQUES

The current indemnity products from health insurers, although easier to administer, do not promote risk sharing among payers, providers, and members, nor is there any incentive for the providers or members to curtail costs or regulate the use of medical services. By continuing to persist with indemnity models, health insurers are not using the various cost and utilization levers available to them which can ensure a more balanced approach to health services. It is imperative that health insurers in India adapt managed care techniques to effectively manage the health financing and delivery process.

In the current indemnity scenario, members do not carry any liability. Health insurers have to introduce risk-sharing models with increased member liability, including benefits such as coinsurance and deductibles. This model will ensure cost sharing with the member on all treatment expenses and consequently regulate the utilization of acute services and inpatient services.

Indemnity plans encourage patients and hospitals to utilize specialist skills and opt for inpatient treatments. There are no checks and controls to prevent excessive utilization of medical facilities. Introduction of utilization control mechanisms (eg, assigning a gatekeeper or primary care physician, a firstlevel prospective utilization review) will significantly address excessive utilization.

India's emerging health insurance sector faces significant operational challenges that managed care models can help to address.

Health Insurance in India: Need for Managed Care Expertise

To evaluate woman-level characteristics associated with timing of follow-up after abnormal mammograms in an integrated healthcare system with an active breast health program.

 The study included women aged 40-84 years who had an abnormal mammogram (20,060 screening and 3184 diagnostic) recommended for follow-up. We compared characteristics of women who received any follow-up evaluation within <7, 8 to 14, 15 to 21, and 22 to 180 days. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) using multivariate ordinal logistic regression.

 The proportion of women seeking care within 7 days was 23% for screening and 69% for diagnostic mammograms. Characteristics associated with later follow-up (>8 days vs <7 days) after an abnormal screening mammogram included being older (OR = 1.15; 95% CI, 1.04-1.26 [age 70-79 years]; OR = 1.31; 95% CI, 1.14-1.51 [age 80 years]), Asian (OR = 1.18; 95% CI, 1.04-1.33), or having a college degree (OR = 1.10; 95% CI, 1.01-1.19). Characteristics associated with earlier follow-up included family history of breast cancer (OR = 0.93; 95% CI, 0.88-0.98), symptoms at time of mammogram (OR = 0.79; 95% CI, 0.70-0.88), or extremely dense breasts (OR = 0.82; 95% CI, 0.69-0.96). For diagnostic mammograms, symptoms at time of mammogram (OR = 0.47; 95% CI, 0.39-0.56) and being obese (OR = 0.79; 95% CI, 0.65-0.98) were associated with earlier follow-up.

 Several woman-level characteristics were associated with timely follow-up after an abnormal screening exam, but only presence of symptoms and being obese was associated with timely follow-up after an abnormal diagnostic exam.

	Within an integrated healthcare system with an active breast health program to facilitate care after an abnormal mammogram, there remains woman-level characteristics associated with timely follow-up.

		Presence of symptoms was associated with timely follow-up after both abnormal screening and diagnostic mammogram.

		Women who were older, Asian, or more educated were less likely to return earlier after an abnormal screening mammogram. Efforts to understand and reduce barriers might be important in these groups of women.

	Approximately 75% of US women report having at least 1 screening mammogram to detect breast cancer.

 Abnormal mammograms, which require additional imaging or surgical follow-up, occur at a rate of almost 10% and 8% of screening and diagnostic exams, respectively.

 There are currently no published national data available for mammography facilities and practices benchmarks for the appropriate timing for receipt of follow-up evaluation after an abnormal mammogram.

	A delay in follow-up can be attributed to characteristics of the woman (ie, not making an appointment), the provider (ie, not communicating the importance of follow-up), or the healthcare system (ie, insufficient scheduling space for prompt return). Failure of 1 or all of these systems results in delayed follow-up by the patient, which may contribute to a delayed diagnosis of breast cancer, later stage disease,

 Woman-level characteristics associated with a delay or lack of follow-up after an abnormal mammogram include being older,

	Evaluating the follow-up of breast health within an HMO setting provides opportunities to monitor women after an abnormal mammographic exam when access to appropriate follow-up care should be equitable. Two prior studies have evaluated follow-up after an abnormal mammogram within an HMO setting,

 but neither study had an active program for breast health, which would identify and schedule care for women with abnormal mammograms. In our study, we investigated woman-level characteristics associated with  follow-up after an abnormal mammography examination in women enrolled in a health plan with an active breast health program.

	Women aged 40 to 84 years who received a mammogram between 1996 and 2003 in Group Health, an integrated delivery system in the Puget Sound region of Washington State, and were enrolled for at least 12 months were eligible for this study. All women had access to free mammography through the health plan, including screening and diagnostic exams. This analysis is restricted to women with an abnormal result from mammography defined as a Breast Imaging Reporting and Data System (BI-RADS)

 assessment of 0 (incomplete), 4 (suspicious abnormality), or 5 (highly suspicious of malignancy). We specifically excluded women recommended for 6-month short-interval follow-up (BI-RADS 3) because we were interested in abnormal exams that required immediate attention. Further, we restricted the sample to women who were recommended to receive additional follow-up with 1 of 4 procedures: (1) biopsy, (2) fine needle aspiration, (3) surgical evaluation, or (4) ultrasound. Women had to be continuously enrolled for 12 months after the abnormal mammogram to ensure adequate information on follow-up. We identified 25,272 and 5074 abnormal screening or diagnostic mammograms, respectively.

 Breast Cancer Screening Program at Group Health

	Since 1985, the Breast Cancer Screening Program at Group Health has sent reminder letters to women to encourage participation in mammography screening.

 To date, the program has mailed more than 500,000 letters. Breast clinic nurses were responsible for much of the follow-up after a mammogram. For 

, the radiologist interpreted the image the next working day after the mammogram. The breast clinic nurses called the patient within 24 to 48 hours after the report was issued with the patient’s result. If a patient had an abnormal result, the breast clinic nurse attempted to schedule the next available appointment for the patient. For 

, the radiologist interpreted the image the same day and reported the result of the mammogram to the woman. A patient with an abnormal diagnostic mammogram would have scheduled the next available follow-up appointment recommended by the radiologist with the breast clinic nurse. Among nonresponders for either screening or diagnostic mammograms, the breast clinic nurse would call the patient to schedule the follow-up appointment.

	At the time of the mammography visit, women completed a self-administered questionnaire,

 which collected information on age, reproductive risk factors, breast screening, medical history, and concerns regarding women’s breasts at the time of the exam. Women completed the questionnaire before knowing the results of their mammogram. The  uestionnaire served as the data source for woman-level characteristics under evaluation.

	Information about follow-up procedures was available through electronic administrative data including pathology, radiology, utilization, and claims databases. Time to follow-up care was calculated from the date of the mammogram until the first date of follow-up care with any of the 4 procedures. Women might have received a procedure that was different from the one recommended, but was still 1 of the 4 procedures of interest. We categorized women according to receipt of follow-up care within <7, 8 to 14, 15 to 21, and 22 to 180 calendar days, based on ability to seek follow-up care within sequential weeks up to 4 weeks.

	This study was approved by the institutional review board at Group Health Research Institute.

	Woman-level characteristics of interest for this analysis included the following risk factors: age, race, body mass index (kg/m2), education, family history of breast cancer in first- or second-degree relatives, self-reported symptoms at the time of mammography, biopsy history prior to the mammography examination, radiologists’ interpretation of mammographic breast density,

 and use of postmenopausal hormone therapy. Women with missing data for any of the risk factors or without follow-up within 180 days were excluded from the  analysis. The final sample size included 20,060 screening and 3184 diagnostic mammograms. Descriptive summary statistics were calculated for each variable by interval of follow-up time.

	We used a multivariate ordinal logistic regression model (ologit) for ordered outcome variables to compare differences in the follow-up timing intervals.

An ordinal logistic model is a model for multinomial data that generalizes the logistic regression model. The model allows the consideration of differences in “early” versus “later” follow-up at each of the 3 thresholds of the outcome. The interpretation of the odds ratio is the comparison of early versus late defined in our analysis as <7 days versus >8 days. All analyses included all potential risk factors and used robust methods to account for clustering for multiple records per woman. Tests for trends were evaluated by entering the ordinal term representing  the categorical variable into the ordinal model. We evaluated the contribution of each variable using a likelihood ratio test, comparing the full model with the reduced model. All hypothesis tests were 2-sided and assumed a type I error rate of alpha = .05.

	All analyses were conducted in Stata/MP 11.0 (Stata-Corp, College Station, TX).

	For women who had an abnormal screening mammogram, the median time from receipt of the exam to followup was 13 days. The proportion of women receiving care within <7, 8 to 14, 15 to 21, and 22 to 180 days was 23.3%, 36.3%, 22.0%, and 18.3%, respectively, among women with abnormal screening mammogram (

). For women who had an abnormal diagnostic mammogram, the median time to further follow-up was 5 days. Further, the proportion of women receiving care within 7, 8 to 14, 15 to 21, and >22 days was 69.1%, 10.6%, 7.0%, and 13.2%, respectively (

	Among women with an abnormal screening mammogram, those more likely to receive follow-up care after 7 days were older (70-79 and >80 years) (P-trend <.001), Asian or Pacific Islander, and college graduates (P-trend = .006). The woman-level factors associated with being more likely to receive follow-up care within 7 days included reporting symptoms at the time of the exam, having a positive family history of breast cancer, and having extremely dense breasts (P-trend = .001) (Table 1).

	Among women with an abnormal diagnostic mammogram, few woman-level characteristics were associated with earlier or later follow-up. Older women had later follow-up than younger women, though the results were not statistically significant at all levels. Women with higher body mass index P-trend = .026) and symptoms at the time of the mammogram were more likely to have follow-up care earlier rather than later.

	This is the largest study to date to investigate the timing of follow-up after an abnormal mammogram. Unique features of this analysis include the available data from an integrated healthcare system and active surveillance by nurses to encourage and assist patients in returning to evaluate the abnormality noted on the mammogram. Further, we had sufficient data to investigate follow-up for both screening and diagnostic mammograms.

	We confirmed that women of older age were more likely to seek care later rather than earlier for both screening and diagnostic mammograms.

 We also detected that having symptoms at the time of the mammogram was associated with earlier follow-up for both screening and diagnostic exams. Our finding is supported by Caplan and colleagues, who determined that women with breast symptoms were diagnosed with breast cancer 6 days earlier than women without breast symptoms.

	In this study, we found that women of Asian/Pacific Islander descent were more likely than white women to delay follow-up care after an abnormal screening mammogram. Karliner and colleagues reported that Asian women did not fully understand their physicians’ reports of their mammograms, particularly Chinese-speaking women.

 It is unclear whether language barriers were responsible for the longer delay in seeking care among Asian/Pacific Islander women in our study, but language interpreters or written materials might help reduce delay in other programs.

	Women with extremely dense breasts were more likely to seek follow-up care quickly compared with women with less dense breasts. Prior studies have not reported on breast density and delay in follow-up care. The sensitivity of mammography in women with extremely dense breasts is only 30% compared with 80% in women with fatty breasts.

 These women are also likely to have an increased risk of interval cancers,

 which likely results in their prompt follow-up.

	In our study, fewer than 1% and 4% of women with abnormal screening and diagnostic mammograms, respectively, did not receive any follow-up within 180 days (data not shown). Other studies have reported lack of follow-up in women after abnormal exams at rates between 8% and 28%.

 The difference in rates of follow-up is likely due to the ability of women to receive additional medical services within our health system at no financial burden and with the assistance of specially trained nurses working in the breast program. The use of computer monitoring to follow up with women postmammography reduces the delays in seeking care.

 Group Health’s Breast Cancer Screening Program implemented active follow-up of women with breast clinic nurses and now uses the electronic medical record to help track women.

	This study was conducted in a well-characterized defined population of women who receive healthcare through an integrated delivery system. There was thorough assessment and follow-up of mammography screening because of the Breast Cancer Screening Program at Group Health. We might have missed some clinical care that occurred at outside facilities that was not submitted for a claim. However, this is unlikely for women with their primary insurance at the health plan, because filing a claim would enable payment for outside services and women would be motivated to do so.

	Clinical guidelines for promptness in obtaining an evaluation after an abnormality is noted on mammography examination are not well established. An assessment of the National Initiative on Cancer Care Quality in the United States suggests that 90% of patients should be seen within 3 weeks of an abnormal screening mammogram for a diagnostic mammogram, within 14 days from needing an open biopsy to cancer diagnosis, and within 5 working days from breast cancer diagnosis to an appointment with the treating physician.

 While we cannot make a direct comparison to the National Initiative for Cancer Care Quality, we can report that 82% of women received the recommended follow-up care within 3 weeks of their abnormal screening mammogram. Further, 69% of women with abnormal diagnostic mammograms received recommended follow-up care within 7 days. In 2004, the National Quality Measures for Breast Centers identified 2 measures of timely follow-up: imaging timeliness of care (ie, time between screening mammogram, diagnostic mammogram, needle biopsy, surgery) and surgical timeliness (ie, time between diagnostic mammogram, open surgical biopsy/excision, initial breast cancer surgery).

 There are characteristics that influence women’s timeliness to follow-up that future interventions may address to reduce delays in breast cancer diagnosis.

 From Group Health Research Institute (KJW, EJAB, SH, DSMB), Seattle, WA; and Department of General Internal Medicine (JGE), University of Washington, Seattle, WA.

 This research was supported by the National Cancer Institute (Dr Buist, Principal Investigator, U01CA63731; and Dr Elmore, Principal Investigator, K05CA104699).

 The authors (KJW, EJAB, SH, JGE, DSMB) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

TRENDS FROM THE FIELD

Woman-level characteristics such as breast symptoms at mammography are associated with earlier follow-up within an integrated health system with an active breast health plan.

Timing of Follow-up After Abnormal Screening and Diagnostic Mammograms

To determine the association between prescription nonadherence and the health of older adults with 1 or more of 5 chronic conditions.

 Analysis of the correlation between prescription nonadherence and the health of older adults.

Data were from a 7-year panel with information on healthcare encounters and prescriptions. We used fixed-effects Cox duration regression models and fixed-effects Poisson count regression models to control for time-invariant factors specific to each subject when examining the impact of nonadherence on time to an emergency department (ED) visit (Cox regression) or number of ED visits (Poisson count regression).

 Nonadherence was associated with subsequent ED visits for hypertension, chronic heart disease, diabetes, and hypercholesterolemia. No significant short-term effects of nonadherence on chronic obstructive pulmonary disease (COPD) were detected. However, long-term effects of nonadherence (using the count regression model with lagged counts) were detected for COPD, as well as for hypertension, diabetes, and hypercholesterolemia.

 Medicinal adherence was important for all 5 conditions analyzed here. A year of nonadherence had the same correlation with ED use as making an older adult 1 year older.

	The correlation between emergency department use and adherence with prescription medicine in older adults with chronic conditions (hypertension, chronic heart disease, diabetes, chronic obstructive pulmonary disease, and hypercholesterolemia) was analyzed.

		Older adults with hypertension and high cholesterol had poor outcomes up to 3 years after their period of nonadherence.

		A year of nonadherence had the same correlation with ED use as making an older adult 1 year older.

	Approximately 88% of persons aged >60 years in the United States take prescription drugs, usually on a regular basis.

 The effectiveness of many prescription drugs is seriously impaired by the failure of patients to follow the recommended regimen of medication. Nonadherence rates are as high as 40% to 86% for some conditions and/or patient groups, resulting in  preventable visits to emergency departments (EDs) and inpatient stays.

 It is estimated that between 4% and 11.4% of all  hospitalizations and 7.6% of all ED visits are related to nonadherence.

 These estimates suggest that increased adherence  could significantly improve health and reduce healthcare costs,

	The incidence of ED visits among adults increases with age. As adults age, they are more likely to arrive at an ED by ambulance,

 and to be admitted from the ED to inpatient care.

Those who are admitted to inpatient care are more likely than younger adults to require care in an intensive care unit.

	In this article we describe a retrospective, longitudinal study of adherence to prescription drugs in the years 1999-2005 among persons aged >60 years who were insured by Medicaid (Arizona Health Care Cost Containment System [AHCCCS]). Each subject had 1 or more of 5 chronic conditions. We compared ED visits among adherent older adults with visits by older adults who were nonadherent.

	The benefits of increased adherence appear large in current studies, but these benefits may be understated because they are based on periods of less than 2 years, omitting the longer-term benefits among chronically ill patients. This study contributes information on the longer-term benefits.

	The subjects include all AHCCCS-insured persons aged >60 years at some time during the study period (January 1, 1999—December 31, 2005) with 1 or more of 5 chronic conditions: hypertension, chronic obstructive pulmonary disease (COPD), chronic heart disease (CHD), diabetes, or hypercholesterolemia (high blood cholesterol). The subjects lived in Maricopa County, Arizona, which includes Phoenix, the fifth largest city in the United States. Approximately 127,916 persons met the selection criteria. The AHCCCS data include details of all healthcare encounters; all filled prescriptions; Current Procedural  Terminology, 

International Classification of Diseases, Ninth Revision,

 and National Drug Code (NDC) codes; and patient demographics. Patient characteristics are shown in 

	The number of records in Table 1 exceeded 127,916 because individuals had multiple conditions. Elimination of records with inconsistent or missing values reduced the number of cases that were used in the estimation (row 2 vs row 1 in Table 1).

	Nonadherence existed when 8 or more days elapsed between the time a prescription was consumed and the time it was refilled, after which we assumed the patient remained nonadherent until the prescription was refilled. We excluded prescriptions that were not refilled within 365 days because we could not know whether this patient was taken off the medicine  by a physician, a change in medication was made, or there was simply a long period of nonadherence.

Estimation of Average Number of Pills per Day.

 The drugs normally prescribed for each condition were identified from the published results of the national Medical Expenditure Panel Survey

 and associated with their respective NDC. The number of pills consumed per day was estimated by dividing the number of pills refilled by the number of days from the original prescription.

	The intended prescription length was estimated by dividing the quantity of pills prescribed by the number of pills consumed per day. The majority of the prescriptions were intended to last 30 days, with a median of 33 days. We assumed that the intended length for estimates from 25 to 38 days was 30 days and reestimated intended lengths. The estimates seemed to be a reasonable approximation of the number of pills per day. Only 5.3% of the 1692 NDCs had estimated values that were not exact multiples of 0.5 or 0.333.

Measuring Emergency Department Visits and Other Variables

	We could not identify the primary reason for an ED visit by older persons with chronic conditions. We assumed that our estimates were reasonable for comparing adherent with nonadherent patients since injuries or acute conditions should be distributed more or less equally between adherent and nonadherent patients.

	Randomization of treatment and control groups is the gold standard for drug studies, but it is impossible to randomize when analyzing adherence to medications for chronic conditions. There is the moral problem of randomly halting medication for some elderly persons but not for others, as well as forcing control group members to take their medications when they may choose to do otherwise. The inability to randomize makes it difficult for cross-sectional studies to separate the effects of adherence from  individual-specific healthy lifestyles and other unobservable personal characteristics.

This sample selection problem may  result in upward bias of the adherence effects, which appear large in current cross-sectional studies. This bias may be partially offset by the omission of longer term effects in the cross-sectional studies, which typically used sample periods of less than 2 years.

	We partially corrected for the problem of unobserved characteristics by using fixed-effects models that identified nonadherence effects for those individuals who changed adherence over time, rather than identifying the effect of nonadherence by using variation between patients at a point in time. While reasons for nonadherence among older persons are not all well known, clear relationships have been found with dose, insurance coverage, complexity of treatment, race, and number of medications.

 Race is of course time invariant, dosage and medications were included in the data, and the persons in the sample had AHCCCS coverage throughout the period and were not subject to copays or deductibles. However, the complexity of treatment was unobserved and changes in complexity could either increase or decrease adherence.

	There are other important time-variant but unobservable influences on adherence, including changes in providerpatient relationships, depression, and changes in cognitive impairment,

 which was a limitation of our study and biased our attempt to estimate the effects of nonadherence. However, regressions of nonadherence on patient and treatment characteristics for AHCCCS patients suggest that our biases might have been relatively small. Medicinal adherence is higher for persons over 65 years of age than younger age groups, suggesting that age-related cognitive impairments are probably not  an important driver of nonadherence for our sample. Treating physician specialty—which also may change over time for a given patient&mdash;is statistically insignificant in explaining adherence. Moreover, comorbidities, which are correlates of the complexity of  care, were generally statistically insignificant in explaining adherence. In particular, for AHCCCS patients, depression exhibited either a statistically insignificant or quantitatively small effect on adherence. Our fixed-effects models improved on previous estimates but could not control for all potential time-variant but unobservable influences. Our results must be interpreted within those constraints.

	We used 2 models—a Cox proportional hazard model and an unconstrained Poisson maximum likelihood model (ie, a count regression model), both with fixed effects for each individual in our sample.

 The Cox model estimated the correlation between nonadherence and the propensity to use the ED, given the time elapsed since the last ED visit. Each observation consisted of the length of time beginning 7 days after a prescription was estimated to be exhausted until the next ED visit. Also included were a dummy variable recording whether or not the given prescription was filled on time, an age variable, and a censoring variable indicating (before an ED visit) nonadherence durations still in process (ie, not yet completed) when the sample was taken.

) function is the baseline hazard function for each individual, including an a

-effect representing all time-invariant covariates. Each individual had his or her own baseline time between ED visits. The baseline duration was assumed to be proportionally shifted by the “exp(

)” term. Because of repeated spells on the same patients, the Cox regression partialed out the hi(t,ai) term for each individual, controlling for their baseline duration and time-invariant factors (including race, sex, family size, genetic predisposition, motivational and ability factors, and the duration of medication use before each person was initially observed in the sample.

Count Regression Estimates of Nonadherence Effects.

 The count regression measured the correlation between the number of adherent days and the number of subsequent ED visits. The mean number of ED visits for the subjects was higher (between 2 and 3 visits per year) than the median number.

	The count regression model estimated longer term trends by including nonadherent outcomes in prior years. A nonadherent ratio (

) for each year in which a patient was continuously enrolled in AHCCCS was calculated as follows:

	                                          (

	Four dummy variables measured variations in ED use with respect to different levels of nonadherence to capture possible nonlinear effects between number of ED visits and levels of nonadherence:

	The fixed-effects, unconditional Poisson count regression, estimated for each condition by maximum likelihood, was:

	where E(ED) is the expected annual number of ED visits; Ø is the overall intercept; ai is an intercept for each individual; μ

 is an intercept for each time period; and NADH

is the set of N.Adher.Ratio coefficients associated with the 4 nonadherence dummy variables defined above, with the nonadherence ratios extending from the current period to ratios lagged up to 3 years (t = 0-3 years). We used dummy variables to allow for an unrestricted nonlinear response between nonadherence and annual ED visits. The NADH coefficients indicate the percent change in number of visits correlated with the respective level of nonadherence compared with patients who were fully adherent, again subject to the potential biases due to our inability to control for  unobservable, time-variant factors.

METHODS

Prescription nonadherence in older patients with chronic health conditions resulted in more emergency department use.

Effects of Nonadherence With Prescription Drugs Among Older Adults

 To determine the longitudinal effect on healthcare costs of multiple chronic conditions among adults aged 18 to 64 years.

 Retrospective cohort assessment of working-age employees and their dependents with continuous coverage in a self-funded health plan from January 1, 2004, to December 31, 2007. Data were obtained from health benefit enrollment files and from medical and pharmacy claims.

Individuals were defined as having chronic conditions based on modification of a published method. The mean annual healthcare costs were estimated for individuals with 0, 1, 2, 3, 4, or 5 or more chronic conditions. The probability of persistence in high-cost categories across years was estimated for individuals in each group.

 Overall, 75.3% of working-age adult enrollees had at least 1 chronic condition, 54.3% had multiple chronic conditions, and 16.5% had 5 or more chronic conditions. The cost of healthcare was higher among individuals with more chronic conditions for all ages. The mean medical cost per year for an individual with no chronic conditions was $2137, while that for an individual with 5 or more chronic conditions was $21,183. Enrollees with more chronic conditions had higher persistence in high-cost categories between years and persisted at these high costs for more years.

 While multiple chronic conditions are common in the population 65 years and older, they are also of great concern for the working-age population. Understanding how to effectively manage individuals with multiple chronic conditions is an important challenge. Effective care management focused on managing the patient as opposed to a condition has the potential to significantly affect healthcare costs.

This study looked at the longitudinal effect on healthcare costs of multiple chronic conditions among working-age adults.

Results showed that 75.3% of working-age adult healthcare system enrollees had at least 1 chronic condition and that 16.5% had 5 or more chronic conditions.

The cost of healthcare was higher for individuals with more chronic conditions.

Enrollees with more chronic conditions had increased persistence in high-cost categories and persisted at these high costs for more years.

Multiple chronic conditions among working-age adults lead to high costs over many years. Understanding how to effectively manage such patients is an important challenge.

February 2011

Excess Hospitalization Days in an Academic Medical Center: Perceptions of Hospitalists and Discharge Planners

Health Insurance in India: Need for Managed Care Expertise

Timing of Follow-up After Abnormal Screening and Diagnostic Mammograms

Effects of Nonadherence With Prescription Drugs Among Older Adults

Effect of Multiple Chronic Conditions Among Working-Age Adults

Abolishing Coinsurance for Oral Antihyperglycemic Agents: Effects on Social Insurance Budgets

Behavioral Health Disorders and Adherence to Measures of Diabetes Care Quality

Psychological Family Intervention for Poorly Controlled Type 2 Diabetes

Outpatient Wait Time and Diabetes Care Quality Improvement