503646
14
JHI0010.1177/1460458213503646Health Informatics JournalBen-Assuli et al.
Article
Using electronic health record systems to optimize admission decisions: T he Creatinine case study
Health Informatics Journal 2015, Vol. 21(1) 73–88 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1460458213503646 jhi.sagepub.com
Ofir Ben-Assuli
Ono Academic College, Israel; Tel-Aviv University, Israel
Itamar Shabtai
The College of Management Academic Studies, Israel
Moshe Leshno
Tel-Aviv University, Israel
Abstract Many medical organizations have implemented electronic health record (EHR) and health information exchange (HIE) networks to improve medical decision-making. This study evaluated the contribution of EHR and HIE networks to physicians by investigating whether health information technology can lead to more efficient admission decisions by reducing redundant admissions in the stressful environment of emergency. Log-files were retrieved from an integrative and interoperable EHR that serves seven main Israeli hospitals. The analysis was restricted to a group of patients seen in the emergency departments who were administered a Creatinine test. The assessment of the contribution of EHR to admission decisions used various statistical analyses and track log-file analysis. We showed that using the EHR contributes to more efficient admission decisions and reduces the number of avoidable admissions. In particular, there was a reduction in readmissions when patient history was viewed. Using EHR can help respond to the international problem of avoidable hospital readmissions. Keywords clinical decision-making, databases and data mining, e-health, electronic health record, information technology health-care evaluation
Corresponding author: Ofir Ben-Assuli, Management Information Systems, Faculty of Business Administration, Ono Academic College, Kiryat Ono 55000, Israel. Email: [email protected]
74
Health Informatics Journal 21(1)
Introduction Interest in information technology (IT) in health care is intensifying rapidly.1 The health-care sector has invested heavily in health information technology (HIT) in recent years2 to improve medical decision-making and increase its efficiency through better medical processes and the integration of patients’ data. Jha et al.3 stated that HIT has the potential to improve the efficiency and effectiveness of health-care providers. In recent years, both successful4,5 and unsuccessful implementations of medical information systems (IS) have been documented worldwide,6–9 involving vast amounts of money. Thus, it is no wonder that the debate over HIT cost-effectiveness has yet to be resolved. This study investigated the clinical and economic implications of using HIT. We explored how accessing historical medical data provided by an electronic health record (EHR) and a health information exchange (HIE) network affects admission decisions in the high-stress environment of emergency departments (EDs). In addition, we explored the role of various sources of information, including local (documented at the same point of care) versus external (documented at other points of care), to assess the contribution of interoperable systems. Specifically, we explored the role that EHR and HIE can play in reducing the number of ED redundant general readmissions and single-day admissions. We suggest that a tiered approach is useful in determining whether HIT has the potential to save costs and in which instances it makes a worthwhile contribution. The following sections provide a literature survey and an introduction to the focal EHR and HIE networks. Section “Data analysis and results” describes the methodology and section “Discussion” details the results of the study. The final sections discuss the implications of the results, provide a discussion, and examine the limitations of the study and avenues for future research.
Previous studies of medical informatics The implications of using HIT at the point of care have been studied from a variety of perspectives. Yet despite the increasing use of these systems by clinicians, there has been little research documenting the clinical and economic implications of their use.10–12 This issue is a familiar one. The productivity paradox suggests that in some fields, the overall contribution of information technologies is not immediately apparent.13 Nevertheless, it has been shown that a lack of information may result in lesser quality of care and unnecessary costs.4,14 This study aimed to fill this gap by examining the impact of online medical systems on the process of decision-making in the stressful ED environment. IT can help achieve the goals of safer, more effective, patient-centered, timely, efficient, and equitable health care. HIT can decrease costs of health care while maintaining or improving quality.15 Walker et al.1 showed that the exchange of information from different sources (known as “interoperability”) between health-care providers enabled a computer-assisted reduction of redundant tests and led to positive financial returns. Ben-Assuli et al.16 evaluated the contribution of EHR and HIE to improvement in medical decision-making processes and concluded that accessing the medical history of patients can lead to better decision-making and outcomes. Although clinicians express a high level of confidence after receiving information,17–20 the introduction of additional information and care options has been shown to increase decision complexity.21 In the case of actual exploitation of information, physicians retrieve only a limited amount
Ben-Assuli et al.
75
of relevant information even without time constraints22 and use EHR systems for fewer tasks than the systems’ support.23 Esquivel et al.24 noted that medical IT is poorly adapted to the health-care social environment and proposed that flexibility in the referral process is necessary for effective system use by staff. Sox et al.25 emphasized the importance of medical history as a way to draw valid, reasonable conclusions regarding medical decisions. Walker et al.1 argued that there is a relationship between the reviewing of medical history and improved medical care, including admission decisions. Goldman et al.26 showed, for instance, that children with abdominal pain received more effective medical care (including better admission decisions) when the physician reviewed their medical history. It is likely that some single-day admissions are unnecessary and could easily be eliminated if physicians had access to proper medical history. These conclusions and more general studies have shown that a clinical support system can efficiently triage patients in an ED using a limited amount of information27–29 and can result in both clinical and economic advantages. Nevertheless, they leave many basic questions unanswered, one of which is the role of EHR and HIE itself in medical decision-making. In addition, the health-care sector needs additional proven results regarding the effect of using HIT on advancing quality of care and improving patient safety. Hence, this study examined the impact of EHR systems and HIE networks as the source of medical history in EDs, rather than other traditional sources of medical information such as hard copies of patient records, physical examinations, or patient complaints.
The EHR IS and its environment In Israel, every citizen is required by law to be a member of one of the four health maintenance organizations (HMOs) that currently exist in the country. This study focused on the largest HMO in the State of Israel, listed as one of the world’s five largest nongovernmental HMOs. This HMO is a nonprofit organization that serves over 3.8 million customers. Its annual budget is funded mostly by the State through National Health Insurance, but also through its customers. The HMO owns seven general hospitals (all surveyed in this study). Hospitals owned by the HMO care for patients insured by other HMOs as well. In 2004, the HMO deployed an interoperable solution covering EHR IS and HIE networks (analyzed in this study). The HMO was the first in Israel and one of the early adopters, internationally, of this health-care IT. Their solution enables health-care organizations to share medical and health information between various points of care that belong to different health-care entities, creating a virtual patient record by logically connecting a group of care providers and organizations and thus eliminating the need to centralize the data or replace existing IS. The system aggregates the documented medical information from all points of care that individual patients visited in the past (including hospitals, community clinics, institutes, labs, etc.). It then performs a context-sensitive integration of the information. This results in a comprehensive, integrated, and real-time virtual patient record available at all points of care of the HMO. As a pure interoperable network, the system only needs a few seconds to gather historical patient data from the other health-care IS at the HMO’s various points of care. The data include patients’ demographics, chronic medication, adverse reactions, detailed lab and imaging results, past diagnoses, healthcare procedures, and so on. However, the hospitals studied here also serve patients insured by other HMOs, but their EHR IS and HIE network only provide full integrative information on patients belonging to the main HMO. Information regarding patients from other HMOs is partial and only includes data documented during previous admissions to the hospital for the current referral and does not include data
76
Health Informatics Journal 21(1)
from community clinic visits. This study also explored this dissimilarity in the nature of information as a function of HMO membership.
The Creatinine case study In this study, we sampled a homogeneous group of patients who were more similar as regards their medical condition than the general public of patients.4 We focused on the subset of ED referrals who had a Creatinine test during their visit. Two different groups were compared: those for whom information was viewed and those for whom information was not viewed. The Creatinine test. Creatinine is a breakdown product of creatine phosphate in muscles and is usually produced at a fairly constant rate by the body (depending on muscle mass). Creatinine is mainly filtered by the kidneys, though a small amount is actively secreted. There is little to no tubular reabsorption of Creatinine. If the filtering by the kidneys is deficient, Creatinine in blood levels rise. The Creatinine blood test is usually ordered along with a blood urea nitrogen (BUN) test to assess kidney function. Both are frequently ordered as part of a basic metabolic panel (BMP) or comprehensive metabolic panel (CMP), which are groups of tests performed to evaluate the function of the body’s major organs. BMP or CMP tests are performed on healthy people during routine physical exams and on acutely or chronically ill patients in the ED and hospitals. If the Creatinine and BUN tests are found to be abnormal or if the patient has an underlying disease, such as diabetes that is known to affect the kidneys, then these two tests may be used to monitor the progress of kidney dysfunction and the effectiveness of treatment. Blood Creatinine and BUN tests may also be ordered to evaluate kidney function prior to some procedures, such as a computerized tomography (CT) scan that may require the use of drugs that can damage the kidneys. Increased Creatinine levels in the blood point to possible diseases or conditions that affect kidney function. Low levels of Creatinine are not common and are not usually a reason for concern. They can be perceived as conditions that result in decreased muscle mass. The standard and normal values for the test range between 0.6 and 1.2 mg/dL.
Material and methods Track log-file analysis Log-files were retrieved from the main HMO for ED referrals from 2004 to 2007 (after the IS was adopted by all seven hospitals and the HIE was established). The log-file consisted of 340,804 admitted patients and 474,310 nonadmitted patients for whom a Creatinine test was conducted. The source of the log-file was seven main hospitals owned by the HMO that used the EHR IS and an interoperable HIE network to share medical information from distributed health suppliers electronically (including external medical clinics, imaging institutions, labs, and hospitals). Medical history was defined as the different types of patient medical histories (described above in the description of the focal IS) viewed by physicians.
Independent variables Viewed medical history. The term “viewed medical history” refers to access to at least one of several medical history categories in the focal EHR IS. Due to lack of information on the number of
Ben-Assuli et al.
77
specific components that were retrieved by the physicians for each case, this was measured as a dichotomous variable, indicating only whether the physician retrieved any information from the EHR IS (1 = history viewed; 0 = history not viewed). Viewed local information. The variable “viewed local information” indicates an examination of medical information created locally within a specific hospital. We coded the variable “viewed local information” 1 if local information was viewed from the EHR IS and 0 if local information was not viewed. Viewed external information (interoperability). The variable “viewed external information” indicates the viewing of medical historical information created outside the specific hospital and provided online by certain health suppliers connected to the HIE network. External information concerned past and present main HMO–insured patients for whom both local and external types of information were available to a certain extent. Coding was 1 if external information was viewed from the EHR IS and 0 if it was not viewed. Health insurance provider. To control for major discrepancies in the quality and the amount of medical information between the main HMO patients and other HMO patients, a dichotomous variable was created (1 = member of the main HMO (for whom full medical history was available via the IS); 0 = member of other HMOs (partial history available)). ED department. This variable represented the specific type of unit where the patient was evaluated in the ED such as internal medicine or surgical. Internal medicine (71.71%), surgical, and pediatrics together amounted to more than 90 percent of the entire sample for which Creatinine test was ordered. Hospital. This variable represented the specific hospital where the patient was evaluated. Each one of the seven hospitals had a different code represented by this variable. Creatinine result. A continuous variable represented the result of the Creatinine test, namely, the level of Creatinine in the blood. This was the result of the Creatinine test ordered by the doctor to determine whether the result was “normal” or not. This variable was measured in milligrams per deciliter.
Dependent variables Admission decisions. Admission decision was defined as a dichotomous variable that described the decision whether or not to admit the patient to the ED (1 for admit decision and 0 for discharge decision). This measurement scale has been used in previous research.30 Single-day admissions. This variable quantified whether a patient, as a result of the decision to admit, was admitted for a single day (coded 1) or for a longer period of time (coded 0). Previous observations showed that the proportion of single-day admissions changed when the patient’s medical history was consulted.4,16 It is likely that some single-day admissions are redundant and could be avoided if physicians had access to proper medical history. Existing scales have shown that such short-term admissions can be reduced.16,31 The measurement scale of single-day admissions filtered out patients who intentionally sought and received treatment involving a single-day admission.
78
Health Informatics Journal 21(1)
Readmission within 7 days. This variable quantified whether a patient was readmitted to a hospital within 7 days since the previous discharge from the hospital (coded 1) or otherwise (coded 0). This measure is widely used as a means of monitoring the efficacy of critical care pathways.32 Decreasing incidences of readmission have increasingly become a goal of caregivers, hospital administrators, and policy makers,33,34 to the point that recent policies seek to penalize doctors for high readmission rates.35 An accepted notion is that the shorter the period between discharge and readmission, the more likely that the patient was discharged prematurely.36 Nevertheless, readmission rates are also used as a proxy for quality of care rendered during the hospitalization.36,37 In addition, it reflects the level of patient safety as part of the whole health-care service.
Data analysis and results Descriptive statistics The first step in the analysis was to calculate the percentages of information viewed in terms of patient characteristics. Table 1 shows that patients’ historical information was viewed via the EHR IS (the majority of which resulted in an admit decision) in only 37.1 percent of all referrals to hospitals. In other words, 62.9 percent of all referrals did not involve any use of EHR. Additionally, physicians mostly used local information (created at the same hospital as the current referral) and made less use of external information (created at another distributed health-care provider), as can be seen in Table 1 (7% external information vs 30.1% local information).
Relationships between the main variables This section presents the statistical analyses of relationships between the dependent variables and the main independent variables: admission decisions, single-day admissions, and 7-day readmissions by Table 1. Characteristics of the study sample and comparison between subjects with medical history viewed versus subjects with medical history not viewed. Characteristics
Total study sample (n = 815,114)
EHR IS was not used (n = 512,987) (62.9%)
EHR IS was used (n = 302,127) (37.1%)
Significance of univariate comparisons
Age (years) Male (%) Insurance (main HMO%) Creatinine result External history viewed (%) Local history viewed (%) Admissions (%) Readmission after 7 days (%) Admission period (days)
51.84 ± 25.73 389,330 (47.8%) 634,097 (77.8%)
49.65 ± 26.28 243,484 (47.5%) 392,962 (76.6%)
55.55 ± 24.3 145,846 (48.3%) 241,135 (79.8%)
14
JHI0010.1177/1460458213503646Health Informatics JournalBen-Assuli et al.
Article
Using electronic health record systems to optimize admission decisions: T he Creatinine case study
Health Informatics Journal 2015, Vol. 21(1) 73–88 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1460458213503646 jhi.sagepub.com
Ofir Ben-Assuli
Ono Academic College, Israel; Tel-Aviv University, Israel
Itamar Shabtai
The College of Management Academic Studies, Israel
Moshe Leshno
Tel-Aviv University, Israel
Abstract Many medical organizations have implemented electronic health record (EHR) and health information exchange (HIE) networks to improve medical decision-making. This study evaluated the contribution of EHR and HIE networks to physicians by investigating whether health information technology can lead to more efficient admission decisions by reducing redundant admissions in the stressful environment of emergency. Log-files were retrieved from an integrative and interoperable EHR that serves seven main Israeli hospitals. The analysis was restricted to a group of patients seen in the emergency departments who were administered a Creatinine test. The assessment of the contribution of EHR to admission decisions used various statistical analyses and track log-file analysis. We showed that using the EHR contributes to more efficient admission decisions and reduces the number of avoidable admissions. In particular, there was a reduction in readmissions when patient history was viewed. Using EHR can help respond to the international problem of avoidable hospital readmissions. Keywords clinical decision-making, databases and data mining, e-health, electronic health record, information technology health-care evaluation
Corresponding author: Ofir Ben-Assuli, Management Information Systems, Faculty of Business Administration, Ono Academic College, Kiryat Ono 55000, Israel. Email: [email protected]
74
Health Informatics Journal 21(1)
Introduction Interest in information technology (IT) in health care is intensifying rapidly.1 The health-care sector has invested heavily in health information technology (HIT) in recent years2 to improve medical decision-making and increase its efficiency through better medical processes and the integration of patients’ data. Jha et al.3 stated that HIT has the potential to improve the efficiency and effectiveness of health-care providers. In recent years, both successful4,5 and unsuccessful implementations of medical information systems (IS) have been documented worldwide,6–9 involving vast amounts of money. Thus, it is no wonder that the debate over HIT cost-effectiveness has yet to be resolved. This study investigated the clinical and economic implications of using HIT. We explored how accessing historical medical data provided by an electronic health record (EHR) and a health information exchange (HIE) network affects admission decisions in the high-stress environment of emergency departments (EDs). In addition, we explored the role of various sources of information, including local (documented at the same point of care) versus external (documented at other points of care), to assess the contribution of interoperable systems. Specifically, we explored the role that EHR and HIE can play in reducing the number of ED redundant general readmissions and single-day admissions. We suggest that a tiered approach is useful in determining whether HIT has the potential to save costs and in which instances it makes a worthwhile contribution. The following sections provide a literature survey and an introduction to the focal EHR and HIE networks. Section “Data analysis and results” describes the methodology and section “Discussion” details the results of the study. The final sections discuss the implications of the results, provide a discussion, and examine the limitations of the study and avenues for future research.
Previous studies of medical informatics The implications of using HIT at the point of care have been studied from a variety of perspectives. Yet despite the increasing use of these systems by clinicians, there has been little research documenting the clinical and economic implications of their use.10–12 This issue is a familiar one. The productivity paradox suggests that in some fields, the overall contribution of information technologies is not immediately apparent.13 Nevertheless, it has been shown that a lack of information may result in lesser quality of care and unnecessary costs.4,14 This study aimed to fill this gap by examining the impact of online medical systems on the process of decision-making in the stressful ED environment. IT can help achieve the goals of safer, more effective, patient-centered, timely, efficient, and equitable health care. HIT can decrease costs of health care while maintaining or improving quality.15 Walker et al.1 showed that the exchange of information from different sources (known as “interoperability”) between health-care providers enabled a computer-assisted reduction of redundant tests and led to positive financial returns. Ben-Assuli et al.16 evaluated the contribution of EHR and HIE to improvement in medical decision-making processes and concluded that accessing the medical history of patients can lead to better decision-making and outcomes. Although clinicians express a high level of confidence after receiving information,17–20 the introduction of additional information and care options has been shown to increase decision complexity.21 In the case of actual exploitation of information, physicians retrieve only a limited amount
Ben-Assuli et al.
75
of relevant information even without time constraints22 and use EHR systems for fewer tasks than the systems’ support.23 Esquivel et al.24 noted that medical IT is poorly adapted to the health-care social environment and proposed that flexibility in the referral process is necessary for effective system use by staff. Sox et al.25 emphasized the importance of medical history as a way to draw valid, reasonable conclusions regarding medical decisions. Walker et al.1 argued that there is a relationship between the reviewing of medical history and improved medical care, including admission decisions. Goldman et al.26 showed, for instance, that children with abdominal pain received more effective medical care (including better admission decisions) when the physician reviewed their medical history. It is likely that some single-day admissions are unnecessary and could easily be eliminated if physicians had access to proper medical history. These conclusions and more general studies have shown that a clinical support system can efficiently triage patients in an ED using a limited amount of information27–29 and can result in both clinical and economic advantages. Nevertheless, they leave many basic questions unanswered, one of which is the role of EHR and HIE itself in medical decision-making. In addition, the health-care sector needs additional proven results regarding the effect of using HIT on advancing quality of care and improving patient safety. Hence, this study examined the impact of EHR systems and HIE networks as the source of medical history in EDs, rather than other traditional sources of medical information such as hard copies of patient records, physical examinations, or patient complaints.
The EHR IS and its environment In Israel, every citizen is required by law to be a member of one of the four health maintenance organizations (HMOs) that currently exist in the country. This study focused on the largest HMO in the State of Israel, listed as one of the world’s five largest nongovernmental HMOs. This HMO is a nonprofit organization that serves over 3.8 million customers. Its annual budget is funded mostly by the State through National Health Insurance, but also through its customers. The HMO owns seven general hospitals (all surveyed in this study). Hospitals owned by the HMO care for patients insured by other HMOs as well. In 2004, the HMO deployed an interoperable solution covering EHR IS and HIE networks (analyzed in this study). The HMO was the first in Israel and one of the early adopters, internationally, of this health-care IT. Their solution enables health-care organizations to share medical and health information between various points of care that belong to different health-care entities, creating a virtual patient record by logically connecting a group of care providers and organizations and thus eliminating the need to centralize the data or replace existing IS. The system aggregates the documented medical information from all points of care that individual patients visited in the past (including hospitals, community clinics, institutes, labs, etc.). It then performs a context-sensitive integration of the information. This results in a comprehensive, integrated, and real-time virtual patient record available at all points of care of the HMO. As a pure interoperable network, the system only needs a few seconds to gather historical patient data from the other health-care IS at the HMO’s various points of care. The data include patients’ demographics, chronic medication, adverse reactions, detailed lab and imaging results, past diagnoses, healthcare procedures, and so on. However, the hospitals studied here also serve patients insured by other HMOs, but their EHR IS and HIE network only provide full integrative information on patients belonging to the main HMO. Information regarding patients from other HMOs is partial and only includes data documented during previous admissions to the hospital for the current referral and does not include data
76
Health Informatics Journal 21(1)
from community clinic visits. This study also explored this dissimilarity in the nature of information as a function of HMO membership.
The Creatinine case study In this study, we sampled a homogeneous group of patients who were more similar as regards their medical condition than the general public of patients.4 We focused on the subset of ED referrals who had a Creatinine test during their visit. Two different groups were compared: those for whom information was viewed and those for whom information was not viewed. The Creatinine test. Creatinine is a breakdown product of creatine phosphate in muscles and is usually produced at a fairly constant rate by the body (depending on muscle mass). Creatinine is mainly filtered by the kidneys, though a small amount is actively secreted. There is little to no tubular reabsorption of Creatinine. If the filtering by the kidneys is deficient, Creatinine in blood levels rise. The Creatinine blood test is usually ordered along with a blood urea nitrogen (BUN) test to assess kidney function. Both are frequently ordered as part of a basic metabolic panel (BMP) or comprehensive metabolic panel (CMP), which are groups of tests performed to evaluate the function of the body’s major organs. BMP or CMP tests are performed on healthy people during routine physical exams and on acutely or chronically ill patients in the ED and hospitals. If the Creatinine and BUN tests are found to be abnormal or if the patient has an underlying disease, such as diabetes that is known to affect the kidneys, then these two tests may be used to monitor the progress of kidney dysfunction and the effectiveness of treatment. Blood Creatinine and BUN tests may also be ordered to evaluate kidney function prior to some procedures, such as a computerized tomography (CT) scan that may require the use of drugs that can damage the kidneys. Increased Creatinine levels in the blood point to possible diseases or conditions that affect kidney function. Low levels of Creatinine are not common and are not usually a reason for concern. They can be perceived as conditions that result in decreased muscle mass. The standard and normal values for the test range between 0.6 and 1.2 mg/dL.
Material and methods Track log-file analysis Log-files were retrieved from the main HMO for ED referrals from 2004 to 2007 (after the IS was adopted by all seven hospitals and the HIE was established). The log-file consisted of 340,804 admitted patients and 474,310 nonadmitted patients for whom a Creatinine test was conducted. The source of the log-file was seven main hospitals owned by the HMO that used the EHR IS and an interoperable HIE network to share medical information from distributed health suppliers electronically (including external medical clinics, imaging institutions, labs, and hospitals). Medical history was defined as the different types of patient medical histories (described above in the description of the focal IS) viewed by physicians.
Independent variables Viewed medical history. The term “viewed medical history” refers to access to at least one of several medical history categories in the focal EHR IS. Due to lack of information on the number of
Ben-Assuli et al.
77
specific components that were retrieved by the physicians for each case, this was measured as a dichotomous variable, indicating only whether the physician retrieved any information from the EHR IS (1 = history viewed; 0 = history not viewed). Viewed local information. The variable “viewed local information” indicates an examination of medical information created locally within a specific hospital. We coded the variable “viewed local information” 1 if local information was viewed from the EHR IS and 0 if local information was not viewed. Viewed external information (interoperability). The variable “viewed external information” indicates the viewing of medical historical information created outside the specific hospital and provided online by certain health suppliers connected to the HIE network. External information concerned past and present main HMO–insured patients for whom both local and external types of information were available to a certain extent. Coding was 1 if external information was viewed from the EHR IS and 0 if it was not viewed. Health insurance provider. To control for major discrepancies in the quality and the amount of medical information between the main HMO patients and other HMO patients, a dichotomous variable was created (1 = member of the main HMO (for whom full medical history was available via the IS); 0 = member of other HMOs (partial history available)). ED department. This variable represented the specific type of unit where the patient was evaluated in the ED such as internal medicine or surgical. Internal medicine (71.71%), surgical, and pediatrics together amounted to more than 90 percent of the entire sample for which Creatinine test was ordered. Hospital. This variable represented the specific hospital where the patient was evaluated. Each one of the seven hospitals had a different code represented by this variable. Creatinine result. A continuous variable represented the result of the Creatinine test, namely, the level of Creatinine in the blood. This was the result of the Creatinine test ordered by the doctor to determine whether the result was “normal” or not. This variable was measured in milligrams per deciliter.
Dependent variables Admission decisions. Admission decision was defined as a dichotomous variable that described the decision whether or not to admit the patient to the ED (1 for admit decision and 0 for discharge decision). This measurement scale has been used in previous research.30 Single-day admissions. This variable quantified whether a patient, as a result of the decision to admit, was admitted for a single day (coded 1) or for a longer period of time (coded 0). Previous observations showed that the proportion of single-day admissions changed when the patient’s medical history was consulted.4,16 It is likely that some single-day admissions are redundant and could be avoided if physicians had access to proper medical history. Existing scales have shown that such short-term admissions can be reduced.16,31 The measurement scale of single-day admissions filtered out patients who intentionally sought and received treatment involving a single-day admission.
78
Health Informatics Journal 21(1)
Readmission within 7 days. This variable quantified whether a patient was readmitted to a hospital within 7 days since the previous discharge from the hospital (coded 1) or otherwise (coded 0). This measure is widely used as a means of monitoring the efficacy of critical care pathways.32 Decreasing incidences of readmission have increasingly become a goal of caregivers, hospital administrators, and policy makers,33,34 to the point that recent policies seek to penalize doctors for high readmission rates.35 An accepted notion is that the shorter the period between discharge and readmission, the more likely that the patient was discharged prematurely.36 Nevertheless, readmission rates are also used as a proxy for quality of care rendered during the hospitalization.36,37 In addition, it reflects the level of patient safety as part of the whole health-care service.
Data analysis and results Descriptive statistics The first step in the analysis was to calculate the percentages of information viewed in terms of patient characteristics. Table 1 shows that patients’ historical information was viewed via the EHR IS (the majority of which resulted in an admit decision) in only 37.1 percent of all referrals to hospitals. In other words, 62.9 percent of all referrals did not involve any use of EHR. Additionally, physicians mostly used local information (created at the same hospital as the current referral) and made less use of external information (created at another distributed health-care provider), as can be seen in Table 1 (7% external information vs 30.1% local information).
Relationships between the main variables This section presents the statistical analyses of relationships between the dependent variables and the main independent variables: admission decisions, single-day admissions, and 7-day readmissions by Table 1. Characteristics of the study sample and comparison between subjects with medical history viewed versus subjects with medical history not viewed. Characteristics
Total study sample (n = 815,114)
EHR IS was not used (n = 512,987) (62.9%)
EHR IS was used (n = 302,127) (37.1%)
Significance of univariate comparisons
Age (years) Male (%) Insurance (main HMO%) Creatinine result External history viewed (%) Local history viewed (%) Admissions (%) Readmission after 7 days (%) Admission period (days)
51.84 ± 25.73 389,330 (47.8%) 634,097 (77.8%)
49.65 ± 26.28 243,484 (47.5%) 392,962 (76.6%)
55.55 ± 24.3 145,846 (48.3%) 241,135 (79.8%)
