Development and Implementation of a Multi-Site Hospice Outcomes ...

286 Journal of Pain and Symptom Management

Vol. 29 No. 3 March 2005

NHPCO Special Article

The National Hospice Outcomes Project: Development and Implementation of a Multi-Site Hospice Outcomes Study Stephen R. Connor, PhD, Susan D. Horn, PhD, Randall J. Smout, MS, and Julie Gassaway, RN, MS National Hospice and Palliative Care Organization (S.R.C.), Alexandria, Virginia, and Institute for Clinical Outcomes Research (S.D.H., R.J.S., J.G.), Salt Lake City, Utah, USA

Abstract Hospice has become a major component of end-of-life care, but little scientific information is available to guide clinicians in knowing when the use of hospice is appropriate, in knowing how to measure the impact of its care, and in knowing which hospice interventions lead to the best outcomes. The National Hospice Outcomes Project (NHOP) arose from the need to identify patient factors and hospice interventions that are associated with better end-of-life outcomes. Clinical Practice Improvement (CPI) methodology allowed us to generate a large comprehensive database that could identify scientifically hospice interventions associated with better outcomes for specific patient populations. The complex interplay of patients, medical and complementary treatments, and families can be evaluated. This paper describes an overview of the research methods used for the NHOP, describes the project’s 13 clinical sites and study population of 1,306 patients, and presents some basic findings from the study. J Pain Symptom Manage 2005;29:286–296. 쑖 2005 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Hospice, database, statistics, outcomes, NHOP

Introduction The hospice movement, which began more than 30 years ago in the United States, has become a major component of end-of-life care. In 2003, more than 3,300 hospice providers cared for approximately 950,000 dying patients.1 Hospice has been suggested as a model for quality end-of-life care. However, few studies

Address reprint requests to: Stephen R. Connor, PhD, National Hospice and Palliative Care Organization, 1700 Diagonal Road, Suite 625, Alexandria, VA 22314, USA. Accepted for publication: January 6, 2005.

쑖 2005 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved.

have compared the effectiveness of hospice care against other end-of-life care, and measurement of hospice outcomes poses unique challenges. Access to hospices is limited to those who are in a recognized “end-stage” of illness, to those who have a prognosis of living less than six months, and to those who agree to receive palliative rather than curative care. Moreover, patients tend to be referred late in their terminal care (2003 median length of stay ⫽ 21 days), thereby missing much of the benefit of hospice care.1 Unfortunately, there is much to do to improve end-of-life care. A landmark study designed to understand problems identified 0885-3924/05/$–see front matter doi:10.1016/j.jpainsymman.2005.01.003

Vol. 29 No. 3 March 2005

The National Hospice Outcomes Project

among severely ill, hospitalized patients found that physicians did not know what type of care patients wanted and that too many patients experienced a painful and prolonged process of dying.2 Key barriers to improving end-of-life care included physicians’ uncertainty over outcomes (prognoses, such as inability to predict the time of death) and their lack of understanding of patients’ wishes for dealing with a terminal illness.3 The results of the study led to a second phase, in which targeted interventions were designed to remedy the problems identified in the first part of the study. Unfortunately, the interventions proved no more effective in the experimental group than in a control group.2 Although this important study failed to identify how to improve end-of-life care, it provided a greater understanding of the complexity of care for patients who are in their last phase of life.4 To improve the culture of dying, clinicians need to understand associations among patient, process, and outcome variables. Clinicians must understand when the use of hospice is most appropriate, how to measure the impact of its care, and which hospice interventions lead to the best outcomes. Only through scientifically valid research will these objectives be met.

National Hospice Outcomes Project The primary goal of the National Hospice Outcomes Project (NHOP) is to identify patient and family factors and hospice interventions that are associated with better outcomes, using data from existing hospice providers throughout the country. To this end, we developed a large comprehensive database containing data from 1,306 hospice patients. The project uses Clinical Practice Improvement (CPI) methodology, which allows for the compilation and evaluation of the complex interplay of patients, medical and complementary treatments, and families in the care of hospice patients. Using CPI methodology, specific components of the hospice care process can be disassembled and analyzed to determine how and to what degree each component contributes to outcomes. This paper provides an overview of the research methods used in the NHOP. Specifically, it describes how the CPI approach was operationalized in this multi-site hospice outcomes study; describes data collection protocols and the database used in the study; describes the

287

project’s clinical sites and study population; and presents some basic findings from the study. Further results from the study will be reported in subsequent papers.

Methods The Human Investigation Committee at one of the study sites (Ministry Home Care-Hospice Program of St. Joseph’s, Marshfield, WI) initially approved the study and each of the other 12 participating hospices either received their own institutional review board (IRB) approval or accepted the Marshfield IRB decision. This study was funded by a series of grants from The Robert Wood Johnson Foundation. The study was directed by the National Hospice and Palliative Care Organization (NHPCO) through an expert panel, with most of the work being carried out by the Institute for Clinical Outcomes Research (ICOR). Drs. Horn and Connor were co-principal investigators for the project.

Clinical Practice Improvement Methodology Clinical Practice Improvement (CPI) methodology was selected for NHOP because it allows the capture of in-depth information about patient characteristics (including clinical signs and symptoms), hospice processes of care, and hospice patient outcomes. Five well-defined steps of the CPI methodology were central to the NHOP. First, a multi-site, multidisciplinary project clinical team was created whose tasks were to identify outcomes of interest; identify individual components of the care process; create a common intervention vocabulary and dictionary; identify key patient characteristics and risk factors; propose hypotheses for testing; participate in analyses; and take ownership of study processes and findings needed to implement clinical practice improvements. The clinical project team was charged with building on theoretical understanding, current research evidence, existing guidelines, and clinical experience about factors that may influence outcomes. Second, the Comprehensive Severity Index (CSI) was used to control for differences in patient severity of illness, including co-morbidities. The CSI, which has been described previously,5,6 is an age- and disease-specific

288

Connor et al.

measure of physiologic and psychosocial complexity comprising more than 2,100 signs, symptoms, and physical findings. CPI uses the CSI as a case-mix adjuster. Third, an intensive data collection protocol was implemented to harvest data on patient characteristics, care processes, and outcomes drawn from hospice medical records and study-specific data collection instruments. The data collection protocols were tested for inter-rater reliability. Fourth, a study database was created that was suitable for statistical analysis. Fifth, hypotheses based on questions that motivated the study originally (derived from previous studies and existing guidelines), and new hypotheses proposed by the clinical project team, were tested using bivariate and multivariate analyses including multiple regression, analysis of variance, logistic regression, hierarchical models, and other methods consistent with measurement properties of key variables.

NHOP Clinical Project Team The NHOP clinical team, comprised members from NHPCO and ICOR, representatives from participating hospices, and experts in pain and dyspnea (see Acknowledgements), provided expert advice to ensure clinical meaningfulness to the NHOP activities and analyses. This team developed and implemented patient selection criteria, designed data collection instruments, obtained IRB approvals, oversaw the data collection process, and participated in analyses. No clinicians or patients received monetary reimbursement for participation.

Vol. 29 No. 3 March 2005

felt that detailed descriptions of pain, dyspnea, and functional status assessments typically are not documented consistently. Therefore, pointof-care assessment/intervention documentation was incorporated into the study design so that all members of the hospice team assessed and described functional status, pain, and dyspnea assessments consistently. Development of the following model assessment tools will be described in more detail in a future publication. Palliative Performance Scale (PPS). The PPS was used to standardize how each hospice assessed functional status. The PPS is a modification of the Karnofsky Performance Scale,7 and assesses function through 5 observer-related domains, including ambulation, activity level/ evidence of disease, self-care, intake, and level of consciousness. Scores range from 100%, which represents complete and normal functionality, through 0%, which represents death. The scale is a reliable and valid tool that correlates well with survival time in cancer patients.8–10 All participating facilities implemented the PPS and completed this form upon hospice admission, again in preparation for interdisciplinary team meetings, and whenever there was a change in a patient’s level of care.

All NHOP patients died while in hospice care and were enrolled in the project after death. For each patient, study data were collected at the point of care and from post-death chart review.

Pain Assessment/Intervention Documentation. A pain assessment/intervention tool was developed for the project by the expert panel and clinical project team. The tool assesses reports from the patient, if possible, and includes behavioral observations and clinician perception. The pain assessment/intervention tool or its equivalent in existing charts was completed for each nursing visit for all hospice patients beginning in March 2002. Completed forms were placed in each patient’s hospice record.

Point of Care Data Collection. An important component of CPI methodology is process, which reflects care the patient receives. It addresses all interventions and management strategies. CPI methodology typically relies on information contained in patient medical records, which trained data collectors abstract following patient discharge. The NHOP clinical project team felt that many identified hospice study variables could be obtained from existing documentation at their respective sites. However, they also

Dyspnea Assessment/Intervention Documentation. A dyspnea assessment/intervention tool was developed specifically for the project by the expert panel and clinical project team. The tool is a comprehensive assessment of patient perception of dyspnea, behavioral observations, education and interventions performed, and clinician perception. The dyspnea assessment/ intervention tool or its equivalent in clinical charts was completed for each nursing visit for all hospice patients, also beginning in March.

Data Collection

Vol. 29 No. 3 March 2005

The National Hospice Outcomes Project

Completed forms were placed in each patient’s hospice record. Training/Reliability. Hospice clinicians were trained to use the assessment/intervention documentation forms via discipline-specific Trainthe-Trainer teleconferences attended by a lead clinician(s) from each hospice. This training was facilitated by the project clinical team using a training manual that included paper and electronic copies of each assessment/intervention documentation form, instructions for completing each form, and definitions for all terms used on each form. Written case studies were included; one case study was used to demonstrate how to complete each form based on a patient scenario. Additional case studies were used to evaluate trainees’ understanding of the instructions by providing examples of how to use the form for different patient scenarios. Following the telephone training session, each clinical leader conducted on-site training sessions for their co-workers. Telephone conference calls were held throughout the two months following training to provide the opportunity for clinicians to discuss implementation issues and ask questions of their peers in other institutions.

Post-Death Chart Review Data Collection Patients were enrolled in the NHOP post death and hospice records of eligible patients were made available to data collectors. Data collectors entered all information obtained from the chart review into the Comprehensive Severity of Illness (CSI) Software System. The CSI Software System allows for the input of severity of illness data and the creation of auxiliary data modules (ADMs), which are sets of study-specific data elements that are collected along with patient severity information. The NHOP clinical project team identified and defined patient (beyond severity of illness), process, and outcome variables to include in the NHOP ADM. Disease-Specific Severity of Illness Data (Signs and Symptoms). The CSI Software System incorporates disease-specific severity algorithms based on a patient’s diseases to produce a 4point measure and a continuous measure (0 to no upper limit) for each diagnosis as well as an overall 4-point and continuous score for the patient that reflects interactions of diseases. To

289

produce these measures, the CSI software asks disease-specific questions (based on each patient’s ICD-9 codes) of a trained data collector who finds ‘answers’ to these questions in a patient’s medical record. For example, ICD-9 code 162 (neoplasm of lung) asks questions such as cough characteristics, adventitious breath sounds, vomiting, weight loss, dyspnea, seizures, confusion, highest pulse, and highest temperature. The data collector then searches the patient record for the most aberrant response for each of these questions. Responses are used to calculate disease-specific severity scores (4-point and continuous) for the malignancy diagnosis and for each of the other diseases (complications and comorbidities). All diagnoses contribute to overall severity scores for the patient, which aggregate signs and symptoms from each of a patient’s diseases. The more abnormal the signs and symptoms, the higher the score, with Level 4 signs and symptoms being catastrophic, life threatening, or likely to result in organ failure. Level 1 indicates a mild form of disease. CSI has been validated extensively in many inpatient, ambulatory, and longterm care settings since 1982.11–18 CSI data were obtained for each physician and nursing visit while the patient was in hospice care. Patient, Process, and Outcome Data. Studyspecific patient, process, and outcome data elements, identified and defined by the NHOP clinical project team, were obtained along with severity of illness information from hospice charts. Most variables contained date and time fields so that they could be associated with other variables in time sequence. The ADM contains over 200 variables, many of which have numerous data entries. Outcome variables in the ADM include pain and dyspnea control during the hospice stay and patient preferences. Patient and process variables include living situation, caretaker needs, preferences, medications, and family interactions. Point-of-care pain, dyspnea, and functional status assessment/intervention information was also collected at this time. Each participating hospice had the option of having the data collector either transfer information from the point-ofcare documentation forms into the CSI software or send the point-of-care documentation forms to the project office for scanning into the project database. Examples of types of data collected are contained in Table 1.

290

Connor et al.

Table 1 Types and Examples of Data in the National Hospice Outcomes Project (NHOP) ICD-9 Diagnosis Codes including co-morbidities and complications CSI Severity of Illness – Selected examples listed High/low BP, pulse, temperature Cough characteristics Adventitious breath sounds Cyanosis Nausea/vomiting Dysphagia Gastrointestinal bleeding (upper and lower) Other Patient, Process, and Outcome Measures - selected examples listed from ADM Demographics – CV history, smoking, alcohol, race, age, weight Living situation – location, caregivers, change in status Nutrition – diet type, appetite, fluid intake Psychosocial interventions Medications Mental status Preferences – place of death, caregivers, advance directives ADL assistance needs Complementary treatments Family education, counseling Financial issues Spiritual interventions Palliative Performance Scale (PPS) Pain Assessment/Intervention Tool information Dyspnea Assessment/Intervention Tool information

Training/Reliability. Each hospice data collector attended a 4-day training session during which efficient collection of accurate data was explained and practiced. Following the training session, each data collector underwent a rigorous manual reliability testing process to ensure complete and accurate data collection that went beyond internal data editing features of CSI software (e.g., features that prohibit entry of non-sensible values). Reliability monitoring was conducted at four points throughout the NHOP to ensure that data accuracy was maintained. An agreement rate of 95% at the criteria level between each data collector and the project training team reliability person was required for each reliability test.

Database Management The comprehensive NHOP database contains all project data, including information from the point-of care pain, dyspnea, and PPS assessment forms. Patients, providers, and hospices cannot be identified directly or through linked identifiers. The entire CSI database was exported to an Oracle database and is being

Vol. 29 No. 3 March 2005

analyzed with SAS statistical software Release 8.2 (SAS Institute, Cary, North Carolina).

NHOP Facilities Thirteen hospices from across the United States participated in the project and contributed data. These facilities were a convenience sample that was selected based on diverse geographic location and willingness to participate. Participants in the project are listed in Table 2.

NHOP Patients This study analyzed data from 1,306 patients from the 13 participating hospices. Each hospice began accruing patients into the study after it had commenced the use of the project assessment/intervention documentation tools (PPS, pain, and dyspnea) for all patients (or all patients within a selected hospice team). Each site enrolled consecutively admitted hospice patients who met the study inclusion criteria. All patients were ⬎18 years of age; were classified as “routine home care” when admitted to hospice regardless of care location; and had died. Each site distributed their 120 study patients equally (40 patients per site per group) among three

Table 2 Participating Hospice Facilities

Hospice

Location

Average Daily Census

Hospice of the Florida Suncoast Ministry Home Care-Hospice Program of St. Joseph’s Houston Hospice Adventist Health Hospice Family Hospice and Palliative Care Home Health and Hospice Care of Nashua Hospice of Michigan Trinity Care Hospice Hospice of the Valley PoPCRN Group Palliative Care Center and Hospice of the North Shore Hospice and Palliative Care of Western Colorado VITAS

Largo, FL

⬎800

Marshfield, WI

26–50

Houston, TX Portland, OR

100–200 51–100

Pittsburgh, PA

201–350

Nashua, NH

26–50

Detroit, MI Torrance, CA Phoenix, AR Denver, CO Evanston, IL

⬎800 51–100 351–500 Various sites 201–350

Grand Junction, CO

101–200

Chicago, IL

201–350

Vol. 29 No. 3 March 2005

The National Hospice Outcomes Project

diagnosis groups (cancer, congestive heart failure [CHF]/chronic obstructive pulmonary disease [COPD], and mental alterations), which included the ICD-9 codes listed in Table 3. Each site further distributed their disease-group patients among the following length of service (LOS) groups: ⱕ 2 days, 3–7 days, 8–14 days, 15–30 days, 31–60 days, and ⬎60 days.

Results The amount of data collected in this study was enormous. The data for 1,306 patients resides in over 200 normalized tables in an Oracle database requiring 1.5 gigabytes of storage space. To date, we have created 266 datasets with data on 10,087 RN and MD visits, 6,974 pain assessments, 6,337 dyspnea assessments, and 16,247 medication orders. In addition, there are between 400 and 600 data entry points for each patient with multiple data entries for many variables for each patient. Of the 1,306 patients, slightly more than half were female (58.6%). The mean (SD) age was 78.66 (12.99) years and the median age was 81 years (range 22 – 103 years). Statistically significant differences in age were present across study sites with mean ages ranging from 74.2 to 81.4 years of age among sites (P ⫽ 0.0002). All sites had some patients who were less than 55 years of age. The racial distribution of the sample was 85.3% white (3.8% of which were Hispanic), 3.6% black, and 2% other races. Race data were missing for 124 patients (9.5%). Patients were enrolled based on diagnosis: 34.5% of patients had cancer, 18.9% had CHF/ COPD, and 20.6% had mental alteration diagnoses. In addition, 8.0% had both cancer and CHF/COPD, 10.0% had CHF/COPD and mental alteration diagnoses, and 6.2% had

Table 3 ICD-9 Diagnosis Groups Eligible for Inclusion in the Study Cancer

CHF/COPD

Mental Alterations

140–239.9

425–425.99 428–428.99 429–429.99 491–492.99 496–496.99

290–389.99 780.01 780.7 783–783.4 783.9 797 799.3

291

cancer and mental alteration diagnoses. Twenty-four patients (1.8%) had all three diagnoses. Patients were further stratified for enrollment by LOS: 12.6% had LOS ⱕ2 days, 23.4% had LOS 3–7 days, 20.0% had LOS 8–14 days, 18.8% had LOS 15–30 days, 13.6% had LOS 31–60 days, and 11.6% had LOS ⬎60 days. The mean (SD) LOS for all study patients was 25.2 days (35.2), and the median LOS was 12 days (range 1–282 days). This mean was lower than that found in most hospice programs due to the LOS stratification requirements of the research design. Approximately 85% of the sample was receiving Medicare (including 5% Medicare HMOs), with the percentage of Medicare recipients varying by site from 78% to 96%. Approximately 12% of the patients were Medicaid recipients, and 21.4% of the patients had private insurance. The remaining patients had VA benefits, were private pay, were not funded, or the payer source was unknown. Approximately 28% of patients had more than one type of insurance. Physicians had been the referral source to hospice for 41.4% of patients. Other referrals were made by hospitals (14.9%), case managers/ social workers (14.2%), care facilities (14.7%), patient/family/friend (9.2%), and home health agencies (3.1%). The mean (SD) number of visits (all providers) was 14.7 (14.0) and the median was 10 visits (range 1–87 visits). Significant site variation was seen, with the mean number of visits ranging from 6.8 to 26.7 by site (P ⬍0.0001). The mean (SD) number of visits by members of the hospice team per day was 0.94 (0.55) visits, and the median was 0.83 visits (range 0.07-4.75 visits). Again, significant site variation was seen: 0.8 to 1.2 visits per day (P ⬍0.0001). The mean (SD) length of time between visits was 1.6 (1.4) days, and the median was 1.2 visits (range 0.21–14.5 days). Site variation ranged from 1.0 to 1.9 days (P ⬍0.0001) (see Table 4). PPS scores ranged from 10 to 90 on admission to hospice. One thousand seventy five patients had one or more PPS scores. The mean (SD) admission PPS score was 36.1 (14.9), and the median was 40. Most patients (91%) had PPS scores less than 50. Mean PPS on admission to hospice varied significantly by site from 26.2– 45.7 (P ⬍0.0001). The mean (SD) number of PPS scores per patient throughout their hospice stay was 2.3 scores (2.8) and the median

292

Connor et al.

Vol. 29 No. 3 March 2005

Table 4 Key Findings by Site N Mean admission PPS score Mean age of subjects % any Medicare % died at home Mean visits per day Mean days between visit

Site 1 99

Site 2 78

Site 3 120

Site 4 120

Site 5 120

Site 6 116

Site 7 81

Site 8 113

Site 9 117

Site 10 120

Site 11 91

Site 12 39

Site 13 92

35.0

33.7

37.5

38.3

35.5

39.0

31.4

45.7

38.7

32.9

26.2

29.3

36.8

78.4

80.8

74.2

78.6

81.4

77.7

81.2

75.6

80.6

80.0

79.8

78.0

77.2

79.8 92.9 0.87

93.6 48.7 1.20

77.5 81.7 1.13

90.0 60.8 0.86

88.3 70.0 0.83

81.0 62.9 0.82

82.7 65.4 0.79

83.2 46.9 0.98

87.2 45.3 0.94

88.3 45.0 0.77

78.0 65.9 1.09

82.0 56.4 0.87

81.5 31.5 1.14

1.80

0.97

1.67

1.44

1.82

1.71

1.51

1.66

1.65

1.88

1.21

1.57

1.38

(range) was 1 (0–20) scores. The mean number of PPS scores by site ranged from 1.1 to 5.7 (see Table 5). Mean PPS scores varied among LOS groups. The shortest LOS group (ⱕ2 days) had the lowest mean admission PPS score (19.7). The mean PPS score increased monotonically as hospice LOS increased; the mean admission PPS score for the ⬎60 day group was 44.7. Mean PPS scores also varied among diagnosis groups. Cancer and CHF/COPD patients had admission mean PPS scores of 41.3 and 35.9, respectively. The mean PPS for patients with both cancer and CHF/COPD was 41.7. Mean admission PPS score was lowest for patients who had a mental alteration diagnosis and no cancer and/or CHF/COPD (26.2). In combination with the other diagnoses, mental alteration patients had admission PPS scores of 32.8 (mental alteration ⫹ CHF/COPD) and 38.3 (mental alteration ⫹ cancer). The pattern of

mental alteration patients having the lowest admission PPS is seen within each LOS group.

Outcome Variables Pain Control. Pain assessment information was collected using traditional hospice documentation for 3,161 (45%) of the pain assessments performed on the 1,306 patients. The project pain assessment tool was used for the remaining 3,806 (55%) pain assessments. Eleven percent (786) of the assessments that were conducted using the pain assessment tool were initial pain assessments, and 30% (2,093) were follow-up pain assessments. Fifty-eight percent of the completed pain assessment tools did not indicate whether the assessment was for initial or follow-up assessment. Interview data, where patients were able to rate their pain on a 0–10 scale, were present for 44% of assessments; behavioral observation data were present

Table 5 Mean Admission PPS Scores by LOS and Diagnosis Group

Cancer CHF/COPD Mental Alterations Cancer ⫹ CHF/COPD CHF/COPD⫹Mental Alterations Cancer⫹Mental Alterations Cancer⫹CHF/COPD⫹Mental Alterations

LOS ⱕ2

LOS 3-7

LOS 8-14

LOS 15-30

LOS 31-60

LOS ⬎60

21.0 n ⫽ 31 18.2 n ⫽ 28 17.6 n ⫽ 25 22.2 n⫽9 17.1 n⫽7 34.0 n⫽5 10.0 n⫽2

34.4 n ⫽ 73 31.7 n ⫽ 41 22.5 n ⫽ 52 35.9 n ⫽ 17 29.6 n ⫽ 22 32.3 n ⫽ 13 20.0 n⫽2

41.2 n ⫽ 82 36.1 n ⫽ 36 25.2 n ⫽ 44 40.7 n ⫽ 14 32.3 n ⫽ 22 35.3 n ⫽ 17 40.0 n⫽3

44.0 n ⫽ 77 39.7 n ⫽ 38 27.8 n ⫽ 50 43.3 n ⫽ 15 39.1 n ⫽ 22 38.5 n ⫽ 13 45.0 n⫽8

48.4 n ⫽ 68 43.2 n ⫽ 28 32.5 n ⫽ 28 53.3 n ⫽ 15 36.7 n ⫽ 12 40.8 n ⫽ 12 30.0 n⫽2

51.6 n ⫽ 44 44.7 n ⫽ 34 34.0 n ⫽ 25 48.5 n ⫽ 13 33.3 n ⫽ 15 51.1 n⫽9 60.0 n⫽2

Vol. 29 No. 3 March 2005

The National Hospice Outcomes Project

for 28% of assessments; and clinician perception of pain was present for 48% of assessments. A total of 16,247 medication orders were analyzed and categorized. Paired pain assessments were used as the unit of analysis rather than pain over the course of a patient’s hospice stay. This allowed for analysis of isolated episodes of good pain control and the associated interventions that were correlated with good control. A total of 3,958 pairs of pain assessments were identified. Analysis of pain data is still preliminary. Dyspnea Control. Dyspnea assessment/intervention/evaluation information was gathered for 6,337 dyspnea assessments. Thirteen percent of these assessments (883) contained end-ofvisit evaluation data; about 1/3 reported improvements with interventions performed, 1/3 no change with interventions performed, and about 1/3 had no information available. The analysis of dyspnea data is still preliminary. Self-Determined Life Closure. Sixty-two percent of patients died in their preferred location; 3.5% did not. Preference of dying location was not known for 33.3% of patients. Patient awareness of their terminal diagnosis was difficult to assess. Information was missing for 29% of the patients, and 5% of patients were either unable to communicate their awareness or their awareness could not be assessed. More than half of patients (54.5%) were aware of their terminal diagnosis, but approximately 6% of patients were not aware of their terminal diagnosis. Most families were aware of the patient’s terminal diagnosis (88%), information was missing for 10%, and it was difficult to assess the remaining patients’ families.

Discussion The diverse nature of hospice patients makes the right match between a patient’s needs and the appropriate services and treatments a challenge. Failure to find the right “fit” can result in too little or too much care for a patient’s individual needs. It is not possible to clinically and fiscally allocate “appropriate” hospice services responsibly if there is insufficient scientific evidence to demonstrate the effectiveness of interventions. By employing the CPI approach,

293

the NHOP assembled a comprehensive database providing the opportunity to examine many aspects of hospice care. The CPI approach helps clinicians determine how and where to improve clinical decisionmaking in order to achieve better patient outcomes. It offers a naturalistic view of hospice treatment by examining what actually happens in the care process. It does not alter the treatment regimen to evaluate efficacy of a particular intervention, as one might in a randomized controlled trial. The CPI approach offers the advantage of large numbers and is able to control for patient differences by taking into account important patient covariates, such as initial severity of illness and functional status. Moreover, CPI’s detailed data on hospice interventions allow researchers to penetrate to the most meaningful level of resolution regarding the types of care rendered—consistent with current knowledge and insights offered by the clinical project team participants. Thus, the CPI approach can answer study questions and hypotheses initially at a fairly basic level of resolution, but also allows researchers to form and test more specific hypotheses with the help of additional insights offered by the clinical project team participants. The Comprehensive Severity Index enabled us to control for complex comorbidities that are common to hospice patients, reflecting more accurately the realities of clinical practice. CSI’s use of very specific, disease-oriented questions produces a highly sensitive measure of severity that could not be produced by using diagnosis and/or procedure codes alone (e.g., Charlson Comorbidity Index)19–21 or a limited, fixed set of physiologic criteria no matter what the underlying diagnoses may be. Diagnosis codes indicate existence of disease; they do not indicate extent of disease. One of the singular contributions of the NHOP has been its attention to defining and measuring interventions used in hospice care. The study attempted to address not only variation in practice but also variation in language and vocabulary that the study uncovered when it attempted to describe hospice practice. A national dataset for hospice22 was used to assist in standardizing measurement of interventions. Overall, hospices in this study varied considerably in how extensively they intervened with patients and families. Also, there are significant

294

Connor et al.

issues with thoroughness of assessment and data collection that are complicated by short length of service and the diminished cognitive status of patients. CPI’s observational and inclusive nature engaged and captured the interest of participating clinicians. When the clinical team determined traditional chart data to be insufficient to capture extensive details about pain and dyspnea assessments, they insisted on a design change. This design change resulted in more extensive clinical intervention documentation than typically found in hospice records. Because of the central role played by the clinical project team in all aspects of CPI, this approach can be characterized as Participatory Action Research – a bottom-up approach to understanding care processes and their impact on outcomes. CPI encourages new findings, even those that challenge conventional wisdom and long-standing practice. Analysis of the NHOP database is in the early stages. Initially it was necessary to organize outcome data into good, bad, or equivocal results. Data directly from patient report is considered the gold standard for analysis, followed by family or clinician observation and evaluation. Once outcomes can be accurately identified, independent variables can be analyzed while controlling for patient characteristics and preferences. This allows researchers to isolate interventions from patient factors to determine the impact of intervention on outcome. People with mental alterations appear to be admitted to hospice after their PPS score has deteriorated more than cancer or CHF/COPD patients, probably due to the Medicare hospice prognostic requirements for admission of very advanced dementia patients. Short LOS patients have lower PPS scores on admission to hospice, indicating that these patients enter into hospice care in late stages of illness. In general, hospices that used the specially designed forms for pain, dyspnea, and performance status assessment had more complete data, while those incorporating the data points into existing documentation systems had more missing data. One of the key outcomes of CPI research is to direct a clinician’s attention to certain aspects of assessment that are more likely to contribute to improved outcomes. Supplemental point-of-care documentation forms, however, do have intrinsic limitations. Add-on

Vol. 29 No. 3 March 2005

documentation to traditional hospice practices increases the documentation burden of frontline staff. Allotted documentation time may not be sufficient to ensure complete documentation of clinical interventions. Intervention documentation form training was conducted via a train-the-trainer approach using a lead clinician in each hospice. Thus, the training of the majority of clinicians was dependent on the expertise and time availability of the hospice trainers. Monitoring of documentation accuracy became an obligation of each hospice. The project team received reports of hospice auditing processes and findings, but did not intervene directly to determine the level of accuracy of documentation form completion. The project was also dependent on each hospice to place completed forms into patient charts and then for the data collector to either abstract information from the forms (seven hospices) and enter it into the CSI Software System or package and send the forms to the project office (six hospices). We believe that the documentation provided, however, does give a representative picture of palliative care provided at these 13 hospices.

Conclusions Palliative hospice care, much like the rest of medicine, lacks an extensive evidence base to support optimal treatments. This project is a first effort to develop a comprehensive patient level database derived from the day-to-day practice of hospice care in the US and from which data can be used to examine the effectiveness of various treatments in use in multiple settings. This article has described the methods used, the study population, and some basic findings from this project. In subsequent publications, we intend to describe evidence to support optimal approaches to assessment and treatment for the management of pain and dyspnea in a patient population with very advanced illness.

Acknowledgments The National Hospice Outcomes Project (NHOP) was conducted under a series of grants from The Robert Wood Johnson Foundation. Evaluation Advisory Panel Members for the project included: Sheila Jacob, RN; Melanie

Vol. 29 No. 3 March 2005

The National Hospice Outcomes Project

Merriman, PhD; True Ryndes, ANP, MPH; Brad Stuart, MD; Joan Teno, MD; and Diana Wilkie, RN, PhD. Hospice site representatives for the project included: Kathy Egan, RN, MA, Hospice Institute of the Florida Suncoast, Largo Florida; Cheryl Thomas and Tracy Matthews, RN, ND, Hospice of the Valley, Phoenix Arizona; Marilyn Follen, RN, MSN and Sue Wilford, RN, MSN, Ministry Home Care Hospice Program of St. Joseph’s, Marshfield, WI; Barry Kinzbrunner, MD and Diane Rapaport, MD, Vitas Healthcare Corporation, Chicago, Illinois; Mindy Lawrence, RN, BSN, OCN, CHPN, Houston Hospice, Houston Texas; Virginia Valentine, Family Hospice and Palliative Care, Pittsburgh, Pennsylvania; Kathy DeLeo-Gittings, RN, Palliative Care Center and Hospice of the North Shore, Evanston, Illinois; Tracie White, RN, Hospice of Western Colorado, Grand Junction, Colorado; Barbara Crane and Janet Schaffer, Adventist Health Hospice, Portland, Oregon; Claire Tehan, MA and Lianne Coleman, Trinity Hospice, Torrance, California; Roxanne Roth and Jeanne Parzukowski, Hospice of Michigan, Detroit, Michigan; Karen Petros, Home Health and Hospice of Nashua, New Hampshire; Jean Kutner, MD and Kieu Vu, PoPCRN Group, Denver Colorado. Thanks also to Carol Spence, RN, MSN, Senior Research Associate and Heather Milstead, Program Assistant at the National Hospice and Palliative Care Organization, Alexandria, Virginia and Bobbie James, Data Systems Specialist at the Institute for Severity Information Systems and the Institute for Clinical Outcomes Research, Salt Lake City Utah.

References 1. National Hospice and Palliative Care Organization. Facts and figures on hospice and palliative care. www.nhpco.org, Accessed 12/21/2004. 2. The SUPPORT Principal Investigators. A controlled trial to improve care for seriously ill hospitalized patients. The study to understand prognoses and preferences for outcomes and risks of treatment (SUPPORT). JAMA 1995;274:1541–1548. 3. Lynn J, Teno J, Harrell F. Accurate prognostication of death: Opportunities and challenges for clinicians. West J Med 1995;163:1–8. 4. Schroeder SA. The legacy of SUPPORT. Ann Intern Med 1999;131:780–782.

295

5. Horn SD, Horn RA. The Computerized Severity Index. A new tool for case-mix management. J Med Sys 1986;10:73–78. 6. Horn SD, Horn RA. Reliability and validity of the Severity of Illness Index. Med Care 1986;24:159–178. 7. Karnofsky DA. The clinical evaluation of chemotherapeutic agents in cancer. In: McLeod CM, ed. Evaluation of chemotherapeutic agents. New York: McGraw Hill, 1949:191–205. 8. Anderson F, Downing GM, Hill J, Casorso L, Lerch N. Palliative performance scale (PPS): a new tool. J Palliat Care 1996;12:5–11. 9. Morita T, Tsunoda J, Inoue S, et al. Validity of the Palliative Performance Scale from a survival perspective. J Pain Symptom Manage 1999;18:2–3. 10. Virik K, Glare P. Validation of the Palliative Performance Scale for inpatients admitted to a palliative care unit in Sydney, Australia. J Pain Symptom Manage 2002;23:455–457. 11. Horn SD, ed. Clinical practice improvement methodology: Implementation and evaluation. New York: Faulkner & Gray, 1997. 12. Horn SD, Sharkey PD, Gassaway J. Managed Care Outcomes Project: Study design, baseline patient characteristics, and outcome measures. American J Managed Care 1996;2(3):237–247. 13. Willson DF, Horn SD, Smout RJ, Gassaway J, Torres A. Severity assessment in children hospitalized with bronchiolitis using the pediatric component of the Comprehensive Severity Index (CSI). Pediatr Crit Care Med 2000;1(2):127–132. 14. Horn SD, Sharkey PD, Buckle JM, et al. The relationship between severity of illness and hospital length of stay and mortality. Medical Care 1991; 29:305–317. 15. Averill RF, McGuire TE, Manning BE, et al. A study of the relationship between severity of illness and hospital cost in New Jersey hospitals. Health Services Research 1992;27(5):587–617. 16. Thomas TJ, Ashcraft MLF. Measuring severity of illness: Comparison of severity and severity systems in terms of ability to explain variation in costs. Inquiry 1991;28:39–55. 17. Alemi F, Rice J, Hankins R. Predicting in-hospital survival of myocardial infarction, a comparative study of various severity measures. Medical Care 1990;28: 762–774. 18. Horn SD, Torres A Jr, Willson D, et al. Development of a pediatric age- and disease-specific severity measure. J Pediatr 2002;141(4):496–503. 19. Carter G, Relles D, Buchanan J, et al. A classification system for inpatient medical rehabilitation patients: A review and proposed revisions to the Functional Independence Measure-Function-Related Groups. [PM-682-HCFA], Project Memorandum prepared for the Health Care Financing Administration by RAND/UCLA/Harvard Center for Health

296

Connor et al.

Care Financing Policy Research. Santa Monica, CA, June 1997. 20. Carter GM, Beeuwkes-Buntin M, Hayden O, et al. Analysis for the initial implementation of the inpatient rehabilitation facility prospective payment system. Report prepared by RAND for the Centers for Medicare and Medicaid Services, 99–154. Santa Monica, CA, June 2002.

Vol. 29 No. 3 March 2005

21. Field TS, Gurwitz JH, Avorn J, et al. Risk factors for adverse drug events among nursing home residents. Arch Intern Med 2001;161:1629–1634. 22. Connor S, Tecca M, Lund-Person J, Teno J. Measuring hospice care: The National Hospice and Palliative Care Organization national hospice dataset. J Pain Symptom Manage 2004;28:316–328.