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Int. J. Public Policy, Vol. 2, Nos. 3/4, 2007

Health information technology: will it make higher quality and more efficient healthcare delivery possible? Jay J. Shen Department of Health Care Administration and Policy School of Public Health University of Nevada at Las Vegas Las Vegas, NV 89154-3023, USA Fax: 702-895-5573 E-mail: [email protected] Abstract: Holding a new promise for improving efficiency and quality and reducing cost, Health Information Technology (HIT) has become the latest national priority. Selecting three evidence-based national quality indicator systems/models as examples, this paper examines relationships between quality of care and HIT as well as their economic implications. The analysis focuses on the three systems’ overall goals; targeted healthcare facilities; data sources; quality indicator measures; data format/standardisation; stages of development; levels of adaptation; and complexity of IT infrastructure including inter-operability, patient involvement, resource requirements, and potential financial gains. The discussion concludes that although enormous challenges are ahead, through joint efforts by all partners and players relating to the healthcare system, the Electronic Health Record (EHR) information system has a potential to fundamentally transform the healthcare delivery to a high-quality and efficient system, which will ultimately benefit patients. Keywords: Health Information Technology (HIT); healthcare delivery; indicators; evidence-based. Reference to this paper should be made as follows: Shen, J.J. (2007) ‘Health information technology: will it make higher quality and more efficient healthcare delivery possible?’, Int. J. Public Policy, Vol. 2, Nos. 3/4, pp.281–297. Biographical notes: Jay J. Shen completed his Bachelor of Science at Nanjing University in China. His MS Degree in Health Policy and Management with concentration on Health Economics at Harvard University in Boston, and his PD in Health Services Organizations and Studies at Virginia Commonwealth University in Richmond. His research interests have focused on disparities including access to care and outcomes/quality of care of racial/ethnic groups, uninsured and socio-economically disadvantaged populations, as well as health economics including health insurance and outcomes, resource consumption and efficiency and public health including obesity and diabetes preventions. He was an Associate Professor and Chair of Department of Health Administration, School of Public Health, Shanghai Medical University in China, and was an Associate Professor at the Department of Health Administration, College of Health Professions, Governors State University in Illinois. Currently, he is an Associate Professor at the Department of Health Care Administration and Policy, School of Public of Health, University of Nevada at Las Vegas.

Copyright © 2007 Inderscience Enterprises Ltd.

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Introduction

Improving quality of care and containing healthcare costs are probably the two biggest challenges for the healthcare system in the USA. Economics textbooks argue that, in general, improving quality requires more resources, which results in higher costs (Feldstein, 2005; Santerre and Neun, 2004). Trade-offs between quality and cost often seem to become necessary in delivering healthcare services because we do not have enough resources, even though we spend about 15% of Gross Domestic Product (GDP) on healthcare (Smith et al., 2005). In fact, during the last two decades, national policies have not focused on both quality and cost simultaneously, choosing instead to focus on one while remaining silent on the other. For example, in the 1980s, the Health Care Financing Administration (HCFA), now changed to Center for Medicare and Medicaid Services (CMS) focused on controlling cost by reviewing Medicare claims to identify unnecessary health service utilisations for the purpose of monitoring Medicare expenditures. Since the early 1990s, the focus has shifted to quality improvement, even though utilisation review was still on the CMS agenda. During the last few years, actions for improving quality and patient safety have been witnessed everywhere in the healthcare system. In the meantime, we have again experienced increasingly higher rates of healthcare expenditures (e.g., 9.3% in 2002 and 7.7% in 2004) than those of the middle and late middle 1990s (e.g., 4.4 % in 1996 and 5.0% in 1997) (Levit et al., 1998; Smith et al., 1999; 2005). This retrospective review makes many of us wonder: Will we ever be able to provide both efficient and high-quality care? The recent rapid development of Health Information Technology (HIT) seems to potentially hold a new promise. HIT has become the latest national priority in improving our healthcare system. In 2004, the federal government unveiled its goals to establish national standards and a health information network to promote health system transformation, enhance quality of care, and generate long-term savings within the next ten years (ONCHIT, 2005). Major stakeholders in healthcare systems (e.g., health policy leaders, patients, purchasers, payers, providers, and business) have shown great interest in IT integration and responded positively to this new national initiative (NBCH, 2005). Several pilot projects have started and more are underway (Cunningham, 2005). It is expected that more resources will be allocated, more policies will be in place, and more implementation programmes will be tested. The healthcare system is enthusiastic about the significant development, as some declare, “It is no longer a question of whether to implement HIT, but how to do it. The days of needing to ‘make the case’ for widespread HIT adoption are over, but now the hard part – getting it done – has begun” (NBCH, 2005). For many of us, the question really comes to this: Has an era of making a fundamental transformation to healthcare delivery with regard to quality improvement and cost containment, backboned by this ‘information technology revolution’, finally arrived? HIT or healthcare informatics, is “the science that studies the use and processing of data, information, and knowledge applied to medicine, healthcare and public health” (van Bemmel and Musen, 1997). As being proved in other industries, using information technology in healthcare delivery has a potential to improve efficiency and reduce costs through actions, such as automatic appointment reservation, checking lab test results online, transaction savings, reductions in malpractice costs, as well as reducing hospital length-of-stay, nurses’ administrative time, drug usage and radiology usage. HIT may also help healthcare providers to reduce medical errors through actions such as Computerised Physician Order Entry (CPOE). Furthermore, it may empower patients to

HIT: will it make higher quality and more efficient healthcare delivery possible? 283 work with providers for disease prevention and chronic disease management, which could greatly improve efficiency and effectiveness (Hillestad et al., 2005). Selecting three evidence-based national quality indicator systems/models as examples, this paper examines relationships between quality of care and HIT as well as their economic implications. The three qualities of care systems are: 1 the Agency for Healthcare Research and Quality (AHRQ) quality indicators, which are based on administrative data 2 the CMS Premier Hospital Quality Incentive Demonstration (PHQID) indicators, which are mainly derived from medical chart abstraction 3 the CMS Doctor Office Quality-Information Technology (DOQ-IT) indicators, which rely on Electronic Health Records (EHR). The discussion will focus on aspects of overall goals, targeted healthcare facilities, data sources, quality indicator measures, data format/standardisation, stage of development, level of adaptation, complexity of IT infrastructure including inter-operability, patient involvement, resource requirement, and potential financial gains.

2

AHRQ’s quality indicators

2.1 Overview of the indicator system AHRQ has made great efforts to develop and disseminate its quality indicator systems in recent years and has made the system fairly sophisticated in terms of indicator calculation, software availability, and user-friendliness. The agency runs the Healthcare Cost and Utilisation and Project (HCUP) that maintains several administrative data sets, two of which are the National Impatient Sample (NIS) and the State Impatient Data (SID). Both datasets are based on inpatient discharges that are in the Uniform Bill-92 (UB-92) form, previously mainly used by hospitals to bill services for Medicare patients. AHRQ creatively uses the readily available hospital inpatient administrative data to monitor quality of care. The AHRQ quality indicators consist of a set of indicators organised into three ‘modules’, each of which measures quality associated with processes of care that occurred in an outpatient or an inpatient setting (AHRQ, 2004a). The first module is the Prevention Quality Indicators (PQIs) or ambulatory care sensitive conditions. The Prevention Quality Indicators represent hospital age and sex-adjusted admission rates for 16 common ambulatory care-sensitive conditions, such as uncontrolled diabetes, diabetes with complications, paediatric asthma, hypertension, congestive heart failure, low birth weight and bacterial pneumonia. Evidence has suggested that with high-quality, community-based primary care, hospitalisation for these illnesses often can be avoided. Therefore, while these indicators are based on inpatient data, they assess the quality of the healthcare system as a whole, especially the quality of ambulatory care in preventing medical complications that can result in hospitalisation. In addition, the indicators provide a quick check on primary care access or outpatient services in a community, as well as help public health agencies, state data organisations, healthcare systems, and others interested in improving healthcare quality in their communities (AHRQ, 2004b). The second module is the Inpatient Quality Indicators (IQIs), which are composed of 34 indicators to reflect quality of care inside hospitals. Examples of these indicators are hospital mortality rates for acute myocardial infarction, stroke, hip fracture, Coronary

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Artery Bypass Graft (CABG), Percutaneous Transluminal Coronary Angioplasty (PTCA) and carotid endarterctomy, procedure utilisation rates of Caesarean section delivery and laparoscopic cholecystectomy, and procedure volumes of pancreatic resection and paediatric heart surgery. These indicators can help hospitals identify potential problem areas and provide the opportunity to assess quality of care inside the hospital (AHRQ, 2004c). The third module is the Patient Safety Indicators (PSIs), which also reflect quality of care inside hospitals but focus on identifying potentially avoidable adverse events (i.e., complications and iatrogenic events) that occur during hospitalisation. Examples of the 26-PSI measures include complications of anaesthesia, foreign body left during procedure, post-operative respiratory failure, and birth trauma/injury to neonate (AHRQ, 2005a).

2.2 Evaluation of the indicator system All of the AHRQ quality indicators are endorsed by the National Quality Forum (NQF), a private, not-for-profit membership organisation created to develop and implement a national strategy for healthcare quality measurement and reporting (NQF members include almost all major healthcare and professional organisations). As of June 2004, 36 states had become a part of the HCUP, an ongoing Federal-State-private sector collaboration to build uniform databases from administrative hospital-based data (AHRQ, 2004a). Meanwhile, more and more states have adopted the AHRQ quality indicators to monitor hospital performance and profile individual hospitals or examine regional variations in quality of care within states. Some states even publish this information online, allowing hospitals’ performance to be reviewed conveniently by their peers, consumers, and other interested groups. Colorado Health Institute, for example, publishes individual hospitals’ quality indicators including clinical condition indicators (i.e., Acute Myocardial Infarction (AMI), CHF, pneumonia, stroke, hip fracture, bleeding-stomach/intestine (GI)), clinical procedure indicators, and service volume indicators. It also provides information for national comparison (Colorado Health and Hospital Association, 2005). Table 1 presents a sample format of Colorado’s hospital profiling that shows a three-year trend of risk-adjusted AMI mortality of participating hospitals. According to the website where real hospital names are posted, those with lower mortality rates than that of the state average may be associated with better quality of AMI care. For example, Hospital 3 had a lower AMI mortality rate than the state average in 2001 and comparable AMI mortality rates with the state average in 2002 and 2003. Individual hospitals can compare themselves to their peers, and consumers and insurers may use the information to select hospital with relatively better quality. Another example comes from Massachusetts, which publishes similar but more detailed quality indicators, including disease-specific hospital mortality and clinical procedure utilisation rates. In addition to the quality indicators, cost and length of stay are also published. One can easily find relevant online information. For each of the quality indicators, a participating hospital is marked higher or lower than, or similar to, the state average, and cost is marked as the top or bottom quartile or the same as the state average. Table 2 shows samples of two indicators of individual hospitals, AMI hospital mortality on the top panel and utilisation of Caesarean section delivery at the bottom panel (Massachusetts Health and Human Service, 2005). For example, Hospital 7 treated 86 AMI patients with average length of stay of five days in 2004. It had a lower AMI

HIT: will it make higher quality and more efficient healthcare delivery possible? 285 mortality than the state average and was within hospitals with middle 50% of cost. As for the C-section utilisation, Hospital D (performing 1180 C-section procedures with average length of stay of four days in 2004), for example, had a higher C-section rate than the state average and was within hospitals with the highest 20% of costs. Through this publicly accessible information, both healthcare providers and consumers are able to compare quality, utilisation, and cost of care across individual hospitals. Table 1

Samples of Colorado risk-adjusted AMI hospital mortality*

Hospital State-wide rate Hospital 1 Hospital 2 Hospital 3 Hospital 4 Hospital 5 Hospital 6 Hospital 7 Hospital 8 ……… ……… Notes:

2001 2002 2003 8.74% 8.38% 7.04 No difference No difference No difference No difference Higher No difference Lower No difference No difference Lower No difference No difference Rates not calculated for hospitals with fewer than 30 cases. Higher Higher Higher No difference No difference No difference Higher No difference Higher

* Modified based on information from the Colorado Health and Hospital Association1

The information technology infrastructure of the UB-92 format data has been almost fully constructed. Most state or data organisations acquire and maintain discharge data from individual hospitals. All data are electronically available at hospitals. All data transmissions are electronic with relatively sophisticated information security and data protection systems. For most hospitals, the adaptation of the AHRQ quality indicators seems to be relatively feasible and may not require too many resources that largely or fundamentally change their existing information infrastructure. To facilitate the use of the quality indicators, AHRQ provides free software that can be downloaded. Any hospital with electronic discharge data can use the software to calculate its quality indicator rates. The earlier version of the software required third-party software, such as the Statistical Analysis System (SAS) or Statistical Package for Social Sciences (SPSS) statistical software; code for calculations in both SAS and SPSS are provided. AHRQ recently released the AHRQ Quality Indicators Windows Application that is a tool to assist quality improvement efforts in acute care hospital settings. Using hospital discharge data from a hospital, the application facilitates the review of individual cases flagged by the AHRQ Quality Indicators (QI) and calculates basic rates for comparison with peers. The single application includes all three of the AHRQ QI modules. The programme is easy to use with step-by-step instructions on loading data and verifying that the data is in the format and has the values needed by the AHRQ QI specifications. Hospitals can look at the results for individual cases, the hospital as a whole, or for subgroups based on patient demographics. Hospitals now do not need to rely on any third-party software to calculate their quality indicators (AHRQ, 2005b).

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Table 2

Samples of Massachusetts two hospital care indicators and cost information§

Hospital State total

AMI mortality

Hospital 1 Hospital 2

★★ $$ 47 Rates not calculated for hospitals with fewer than 30 cases. ★★ $$$ 887

Hospital 3

Cost

Total number of cases 11,429

Average length of stay 5 4

Hospital 4

★★

$$

100

5

Hospital 5

★★

$$$

953

3

Hospital 6

★★

$$$

252

4

Hospital 7

★★★

$$

86

5

Hospital 8 ………... ………

★★

$

42

5

C-section utilisation State total Hospital A Hospital B Hospital C Hospital D Hospital E Hospital F Hospital G Hospital H ……… ………… Notes:

↔ ↓ ↓ ↑ ↔ ↓ ↑ ↔

Cost $$ $$ $$ $$$ $$ $$$ $$ $

Total number of cases 17 450 168 777 125 1 180 421 1 831 286 231

Average length of stay 4 4 4 4 4 4 4 4

★ Mortality significantly higher than state average ★★ Mortality as expected ★★★ $ $$ $$$ ↑ ↔ ↓ AMI C-section §

Mortality significantly lower than state average Hospitals with lowest 25% of costs Hospitals in middle 50% of costs Hospitals with highest 25% of costs Utilisation significantly higher than state average Utilisation same as state average Utilisation significantly lower than state average Acute Myocardial Infarction Caesarean Section Delivery Selected from and Modified based on ‘Massachusetts Health Care Quality and Cost Information, by Hospital, Heart Attack (AMI) Without Transfers from Other Hospitals’ and ‘Massachusetts Health Care, by Hospital Name, Caesarean Section Volume and Utilisation’ available at www.mass.gov/healthcareqc.

HIT: will it make higher quality and more efficient healthcare delivery possible? 287 Although the AHRQ QI possess such advantages as being based on readily available administrative data, having the standardised UB-92 form, including all discharge censuses of individual hospitals, being easy to learn and relatively inexpensive to use, they inherit limitations associated with the use of administrative data. Some major limitations include lack of clinical information, limited risk adjustment capabilities, the potential impact of variations in coding practices, absence of provider-patient interactions, and potential impact of practice patterns (such as the tendency to perform a procedure in an outpatient setting), which may discount the validity of results when hospitals are compared with each other (AHRQ, 2004a). In addition, the AHRQ indicators are only based on hospital discharges, which, sometimes, may not be able to catch the full episode of hospital care. Finally, given that they are not the focus of the original aim, economic gains of adopting the indicators have not been studied.

3

CMS’s PHQID indicators

3.1 Overview of the indicator system The Center for Medicare and Medicaid Service had developed hospital-based quality indicators in the 1990s for several acute conditions (e.g., AMI, CHF, stroke including Transient Cerebral Ischemia (TIA), and pneumonia) during the CMS PRO 5th and 6th Scope of Work (SOW). The PRO in each state, funded by CMS, has done various quality improvement projects with participating hospitals, using the quality indicators as a guideline in their practice. During recent years, it has been observed that overall, quality indicators of hospitals have been improved and variations among hospitals have been reduced (CMS, 2005a). Recently, CMS, to explore the pay-for-performance mechanism, launched the PHQID in which 265 hospitals in the nation participated. Quality measures used by the demonstration are based on clinical evidence and industry-recognised metrics. CMS incorporates ten indicators from an American Hospital Association (AHA) initiative and the National Voluntary Hospital Reporting Initiative, 27 National Quality Forum (NQF) indicators, 24 CMS 7th Scope of Work indicators, 15 Joint Commission on Accreditation of Healthcare Organizations (JCAHO) Core Measures indicators, three indicators proposed by the Leapfrog Group, and four AHRQ patient safety indicators (CMS, 2005b). Eventually, the demonstration selects a total of 34 quality indicators including nine for AMI, eight for Coronary Artery Bypass Graft (CABG), four for heart failure, seven for Community-Acquired Pneumonia (CAP), and six for hip and knee replacement. Table 3 samples descriptions of the nine AMI indicators, from which one can see that detailed clinical information is required to calculate all indicators except the last one, in-hospital mortality (CMS, 2005b). In addition to the hospital quality indicators, CMS has also initiated various quality improvement efforts targeting nursing homes (e.g., percent of patients with delirium, percent of patients with pain, and percent of patients with mobility decline) and home care (e.g., improvement in bathing, improvement in management of oral medications, and discharge to community) (CMS, 2005c; CMS, 2005d).

288 Table 3

J.J. Shen Descriptions of the CMS AMI quality indicators*

Indicator

Description

AMI 1

Percentage of patients without aspirin contraindications received aspirin within 24 hours before or after hospital arrival

AMI 2

Percentage of patients without aspirin contraindications are prescribed aspirin at discharge

AMI 3

Percentage of patients with Left Ventricular Systolic Dysfunction (LVSD) are prescribed Angiotensin Converting Enzyme Inhibitor (ACEI) at discharge

AMI 4

Percentage of patients with history of smoking cigarettes during the year prior to arrival received smoking cessation advice or counselling

AMI 5

Percentage of patients without beta blocker contraindications are prescribed a beta blocker at discharge

AMI 6

Percentage of patients without beta blocker contraindications are prescribed a beta blocker within 24 hours after hospital arrival

AMI 7

Percentage of patients receive thrombolystic agent in patients with ST segment elevation or LBBB on the ECG within 30 minutes after hospital arrival

AMI 8

Percentage of patients receive PCI within 120 minutes after hospital arrival

AMI 9

Percentage of patients died in hospital

Notes:

* Modified based on ‘the Premier Hospital Quality Incentive Demonstration: Clinical Conditions and Measures for Reporting’2

3.2 Evaluation of the indicator system As mentioned above, the CMS quality indicators are developed based on the existing indicators endorsed, recommended, or used by national professional organisations (e.g., NQF, NCQA, AHA) and the leading government agency for quality of care (i.e., AHRQ). They focus on the process of care and most of them are used as guidelines in clinical practice. Abundant empirical evidence has shown that these indicators enable us to examine certain aspects of quality of care (AHRQ, 2002; 2006). They also contain more detailed clinical information than the AHRQ indicators about the episode of care, such as times of the patient’s symptoms, conditions, and clinical interventions including medications, clinical procedures and consultations. As a result, it is more feasible to apply inclusion and exclusion criteria in the calculations. Unlike the AHRQ indicators, which only signal a potential clinical area but require further investigation to confirm the signals, the CMS hospital quality indicators are based on medical records and are calculated according to clinical algorithms, which, in general, give them better clinical integrity than those of the AHRQ indicators. Applications of this type of medical chart-based quality indicators on a regular basis are hindered by two constraints, both of which relate to resources needed for the data collection and not to the information technology capacity. First, although CMS uses MedQuest, a standardised software module for medical chart abstraction, to train data collectors and do the data entry, data collection is expensive. JCAHO, adapting its hospital core quality measures largely from CMS, estimated the time of data collection in ten hospitals in Rhode Island. Each hospital collected data for three core measure sets (AMI, Heart Failure (HF), and CAP) for a period of 12 months. They used a modified version of the CMS MedQuest data collection tool, which records the actual time spent

HIT: will it make higher quality and more efficient healthcare delivery possible? 289 abstracting each chart. Results show that median time per hospital, per measure set, per chart was 15.6 minutes (0.26 hours) per AMI case, 12 minutes (0.20 hours) per HF case, and 12.6 minutes (0.21 hours) per CAP case. JCAHO also adjusted the ‘actual’ time to provide a better estimate of total time and money per core measure set selected. The actual times were adjusted to account for time spent identifying records (i.e., locating and gathering records for data abstraction) and for productivity limitations (i.e., confounding activities that preclude absolutely efficient data abstraction). The adjusted times, therefore, were 27 minutes (0.45 hours) per AMI case, 22.2 minutes (0.37 hours) per HF case, and 23.4 minutes (0.39 hours) per CAP case (JCAHO, 2005). The second constraint is the ad hoc nature of the data collection. Unlike the UB-92 administrative data forms required for billing, which are collected daily and include all discharges, the billing required UB-92 form administrative data that are collected daily and include all discharges, medical record abstraction goes with specific quality improvement initiatives. Disease-/condition-/procedure-specific sampling of medical records is necessary. Although the data collection does not require cutting edge information technology, it is labour intense. Long-term data collection is challenging for hospitals once the quality initiative project ends. Given that 30–100 cases (depending on specific indicators) are needed to calculate the quality indicators and most of medical record abstractors are registered nurses, the cost of data collection is high (Hofer et al., 1999). In fact, most of CMS’s quality improvement projects with regard to data collection and quality indicator calculation, so far, have been conducted by Quality Improvement Organizations (QIOs). Considering only several clinical conditions are currently covered by existing quality indicators, as many more indicators are expected to be identified and adapted in the future, few hospitals seem to be financially capable to do the disease-specific data collection regularly. In addition, similar to the AHRQ indicators, the nature of the CMS indicators data collection does not reflect patient interactions and the economic gains are unclear.

4

CMS’s DOQ-IT indicators

4.1 Overview of the indicator system Although quality indicators have been adopted in hospitals and other healthcare facilities, physician groups/offices had not been directly engaged until recently. Having focused on quality initiatives in hospitals, nursing homes, and home care for a few years, CMS’s recent efforts have started targeting quality of physician services. One of these efforts is the DOQ-IT, a two-year Special Study demonstration to improve quality of care, patient safety, and efficiency of services provided to Medicare beneficiaries by promoting the adoption of electronic health records and information technology in primary care physician offices (DOQ-IT, 2005; NBCH, 2005). The set of healthcare quality measures that will be used to track progress in achieving the goals of the DOQ-IT project was developed by CMS and several partners including the AMA-led Physician Consortium for Performance Improvement, the National Diabetes Quality Improvement Alliance (NDQIA), and National Committee for Quality Assurance (NCQA). The quality indicators include seven Coronary Artery Disease (CAD) measures (e.g., LDL cholesterol level and blood pressure), eight Diabetes Mellitus (DM) measures (e.g., HbA1c management, eye exam), eight HF measures

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(e.g., Left Ventricular Function (LVF) assessment and beta-blocker therapy), three hypertension measures (e.g., blood pressure screening and blood pressure control), and 12 preventive care measures (e.g., pneumonia vaccination, colorectal cancer screening, and breast cancer screening) (AMA, 2005; DOQ-IT, 2005; NCQA, 2005). The DOQ-IT data consist primarily of patient-observation data related to the physician’s practice. All data collection will rely on the electronic health record. Physician office participants need to implement an EHR system in their offices in order to participate. Furthermore, in order to transmit data from the physician offices into DOQ-IT, the EHR system must transmit data pursuant to the standard of Health Level 7 (HL7), a software that provides standards for the exchange, management and integration of data that support clinical patient care; and the management, delivery, and evaluation of healthcare services. The HL7 requires the use of standard coding systems (e.g., ICD-9-CM, CPT4, NDC, LOINC, and SNOMED) to identify diagnoses, medical exclusions, vital signs, drug history, observations, lab test results, and clinical procedures. Currently, the HL7 has a capacity for data exchange and inter-operability between healthcare systems (e.g., hospitals, physician offices) and ancillary systems, such as billing, medical imaging, scheduling, lab, and pharmacy whereas DOQ-IT will be another ancillary system (Hennessey, 2005). Figure 1 illustrates the process of how patient data travel to DOQ-IT. The physician office uploads data through a standardised EHR platform. After data are extracted from the EHR system and written to an output file, the physician office can log onto QualityNet Exchange (QNet), a platform that provides secure, interactive applications for the exchange of privacy data between healthcare providers and the QIO (QualityNet Exchange, 2005). The data then will be loaded into the DOQ-IT data warehouse, where they will be stored for reporting purposes. DOQ-IT warehouse will receive, review and validate electronically transmitted information regarding practitioner performance and identify opportunities for improvement. Physician office participants will have the ability to access feedback reports through the platform on quality measures. These reports will allow participating physician groups to monitor their performance and receive state and national comparison data on the quality indicators (DOQ-IT, 2005; Hennessey, 2005).

4.2 Evaluation of the indicator system The DOQ-IT quality indicators, similar to the CMS hospital indicators, are well developed and some of them have been widely used by governmental agencies and national organisations (e.g., CMS and NCQA). Most of the indicators’ calculations require detailed medical information, which will be supported by EHR. Given that recent studies have highlighted the potential for IT to improve the quality, safety, and efficiency of healthcare, those IT-based indicator systems possess tremendous advantage over the other two quality indicator systems. Table 4 summarises the key features of the three quality indicator systems. If well established and implemented, the DOQ-IT system will bring a fundamental transformation in quality of care improvement – enabling immediate and universal access to patient information about all activities that happen during the health services delivery process. As some IT experts describe, “The EHR incorporates all provider records of encounters where the patient has received medical care. Documentation of many events occurs with the inpatient experience – recording of encounters with clinicians, treatment received, test results, and medications ordered. Following his or her discharge, the patient may have office visits with practitioners

HIT: will it make higher quality and more efficient healthcare delivery possible? 291 and receive ongoing care ordered by these practitioners. The aggregate recording of these encounters and interactions with the patient (across all involved healthcare enterprises) comprises the EHR” (Upham, 2004). The patient’s clinical and treatment information can be conveniently shared by multiple providers and by both the provider and the patient, which can greatly enhance patient-provider communication (Tang and Lansky, 2005). Figure 1

HER-based information flow of DOQ-IT

EHR System in Physician Office 1) Data collection 2) Using HL7 for data extraction 3) Data written to Output File for Submission

Export HL7 file

QualityNet Exchange Data Exchange Platform

File Upload

Quality Measure Reports

DOQ-IT Data Warehouse 1) Data receiving and reviewing 2) Data processing 3) Report generating

Adopting EHR builds up a comprehensive information technology infrastructure for all healthcare providers and facilities. The ability to access patient information, as well as decision support and reference data hold the promise of improving the efficiency and effectiveness of healthcare delivery (Upham, 2004). In fact, if widespread use of EHR becomes reality, the three quality indicator systems, and all other existing systems, can be easily integrated into one comprehensive system, in which monitoring quality of care can be achieved at any point of healthcare delivery in any healthcare facility including the patient’s home. Furthermore, obtaining information for calculating any new quality indicators emerging in the future will require little effort because the comprehensive

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information technology infrastructure already exists. Health provider quality of care profiling and efficiency profiling, patient scrutinising, and policy monitoring can be done regularly and in a timely fashion. As a result, problem detecting and performance improvement will become much more effective. Early empirical findings are encouraging with regard to reducing medication errors, increasing formulary adherence, improving healthcare productivity, and encouraging patient participation (Baron et al., 2005; Bell and Friedman, 2005; NBCH, 2005). Table 4

Comparison of key features of the three quality indicators systems Quality of care system/model CMS PHQID Quality improvement and efficiency, pay-for-performance

Dimension Main aim

AHRQ Quality improvement

Healthcare facility targeted

Hospital

Patients affected

All hospitalised patients

Data source

Hospital discharges

Data elements

UB-92, ICD-9

Amount of medical information Clinical integrity Information technology Resource requirement

Limited

Moderate

Detailed information about patient medical history and current episode of care including medications Enormous

Moderate Simple software available Less expensive

Good Simple

Excellent Very complicate

Stage of development

Sophisticated existing quality indicators, new indicators to be expanded and developed

Manageable on ad hoc base; very expensive on regular basis Sophisticated existing quality indicators, new indicators to be expanded and developed

Level of adoption

Moderate

Patient communication

None

Moderate: depending on CMS/QIO’s quality improvement projects None

Potential financial gains

Unclear

Unclear

Very expensive especially at the beginning Existing quality indicators under testing in demonstration projects Pilot stage, relying on level of EHR adoption Patients having access to their own medical information Limited early results, promising projections

Hospital, community health centres, nursing home, home care Directly to Medicare patients, Medicaid patients, indirectly to other patients Facility discharges, outpatient claims, medical chart review UB-92, ICD-9, CPT, lab test, medication, medical consultation

CMS DOQ-IT Quality and efficiency improvement, pay-for-performance Physician office

Medicare patients having physician visits Electronic health record

HIT: will it make higher quality and more efficient healthcare delivery possible? 293 The key to executing this quality-indicator system, however, is to build the standardised EHR system in physician offices, many of which will start from a ground zero infrastructure building because physician offices remain largely unengaged regarding the adoption and use of e-health technologies (DOQ-IT, 2005). To this date, only 9% of health organisations are well prepared to adopt the EHR system (NBCH, 2005) and 27% of physicians are using various unstandardised EHRs (Bates, 2005). Healthcare providers face enormous challenges to adapt the EHR systems. Examples of implementation barriers include employee training and education, employees’ frustration, and psychological burn-out, changes in existing practice management systems and physician-patient interaction, changes in facility work flow, productivity decline, and insufficient technical support from IT vendors (Baron et al., 2005). Examples of policy and regulatory barriers include privacy rules and security regulations, state and regional policy and regulation variations, patient tracking, identification and data availability (Gottlieb et al., 2005). Because establishing a universal healthcare information system requires cutting edge technology and enormous resources, perhaps the two biggest hurdles are information technology itself and financial resource requirements. The major technical hurdles include lack of uniform standards for documentation of clinical services, concerns about inability to align workflow with a standardised EHR, lack of inter-operability and standardised technical platforms to support EHR, lack of a universal information network with uniformed standards, and system maintenance (Baron et al., 2005; Bates, 2005; Cunningham, 2005; Hammond, 2005; NBCH, 2005; Upham, 2004). The major financial barriers include lack of support for start-up expenses or reimbursement for implementation costs, high maintenance costs, and lack of easily perceived economic gains and return on investment (Baron et al., 2005; Hackbarth and Milgate, 2005; Upham, 2004). Despite the fact that estimation of economic gains of widespread adoption of EHR due to improved efficiency and safety seems to be promising (more than $81 billion a year) (Hillestad et al., 2005), it could be over-optimistic (Goodman, 2005). Since it may need an incentive of $12,000–$24,000 per full-time physician per year for the EHR adoption (Miller et al., 2004), limited available empirical results indicate that existing reimbursement systems do not encourage the EHR adoption because the current reimbursement mechanism rewards the volume more than it does the quality of services (Baron et al., 2005; Miller et al., 2005).

5

Actions and strategies to overcome barriers

A recent Institute of Medicine (IOM) report, ‘Fostering rapid advances in health’, called for significant reforms in the practice and organisation of medicine and recommended that the federal government undertake actions to stimulate innovation in the adoption of health information technology systems (IOM, 2002). Federal government agencies, collaborating with state and local governments, professional organisations, advocate groups, purchasers, business, and IT industries, have taken the lead to start this national HIT transformation. The newly established Office of the National Coordinator for Health Information Technology (ONCHIT) is coordinating efforts to develop a National Health Information Network (NHIN) to address issues related to IT standardisation and networking (Cunningham, 2005). The NHIN, in five years, will attempt to have

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comprehensive functional capabilities of electronic result review of ordered tests, electronic health records, Computerised Physician Order Entry (CPOE), electronic claim submission, electronic eligibility verification, secure electronic patient communication, and electronic prescription acceptance by pharmacies (Kaushal et al., 2005). AHRQ will allocate $139 million grants in HIT-related research and implementation projects. CMS has been implementing pay-for-performance pilot projects in physician office, hospital, and nursing home settings, among which the DOQ-IT demonstration intends to stimulate innovation in the adoption of IT systems including the provision of financial rewards to the EHR-based physician services (DOQ-IT, 2005; Friesen, 2005). In order to reduce the financial burden for small practices, the ONCHIT and CMS will offer pay-for-use benefits to small health facilities that adopt EHRs (Cunningham, 2005). The national goal is to have 50% of the population in the nation covered by EHRs by 2014 (Cunningham, 2005; NBCH, 2005). Can we imagine what the health service delivery will look like if the goal is reached? As a matter of fact, we may not need imagination. Here, let us cite two anecdotes, written by a healthcare provider group and a patient. The first was written by a small physician group (four internists) in Philadelphia, Pennsylvania, that voluntarily adapted the EHR system without any external financial aids in July 2004. The second was written by a patient who received care at a large multi-specialty group practice in Palo Alto, California, where a Patient Health Record (PHR) system has been operating since 2002. “Despite the difficulties and expense of implementing the electronic health record, none of us would go back to paper. We find ourselves able to be better physicians: We communicate more quickly and clearly with patients… more efficiently to specialists, and spend less time paging through charts to find out what the previous cholesterol values … had been. Practicing with a computer in hand allows us to access current health information for our patients and ourselves without having to leave the room or interrupt the flow of a patient encounter. We have already caught a glimpse of population health possibilities when, on the same day as the withdrawal of valdecoxib from the market, we were able to identify and send letters about the withdrawal to the 16 patients in our practice who were taking the drug. We expect soon to produce a list of patients with diabetes so that we can audit their care and see how well we meet our care standards.” (Baron et al., 2005) “I always check my lab results … because I’ve had ready access to this information, I was able to tailor a diet specifically to adjust my blood lipids … I saw the improved test results two weeks ago. Not only was I hugely successful (triglycerides from 333 to 85), but I lost 20 pounds, too. Having my lab results online was tremendously helpful.” (Tang and Lansky, 2005)

Four years ago, in the Crossing the Quality Chasm: A New Health System for the 21st Century, the Institute of Medicine (IOM, 2001) issued its six aims: providing safe, effective, patient-centred, timely, efficient, and equitable healthcare. Not too long ago, achieving these six extremely challenging aims seemed to be too far away, but the recent fast development of quality of care and health information technology may make us become more optimistic. A clear vision and greater and continuous joint efforts from all parties/players at all levels may shorten the timeline of building up the ultimate healthcare with high efficiency and best quality that will ultimately benefit our patients and population. When that day arrives, some parts of current health economics textbooks may need to be rewritten.

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Notes 1 2

Discharge Data Program available on http://www.hospitalquality.org/, accessed on 25 November 2005. http://www.cms.hhs.gov/quality/hospital/