since 2005 , the non - profit midsouth ehealth alliance has governed and managed hie services among 16 major healthcare provider organizations in the memphis , tennessee , usa , metropolitan area .
all 12 major hospitals provided hospital discharge summary notes , laboratory data , pathology reports , radiographic reports , other transcribed notes , and a range of other clinical and administrative documents .
all participating clinics provided demographic information , registration information , and a limited number of clinical data types .
clinicians began accessing health information exchange data in their eds in may 2006 and later obtained access on hospital wards and in ambulatory clinics .
hie access in eds was phased in over the study period ; all major hospital ed had access to hie before collecting hie use data .
as of 1 october 2010 , clinicians had access to over 7.5 million encounter records on 1.7 million patients , 4.9 million chief complaints , 45 million laboratory tests , 5 million radiology reports , and 2.1 million other reports and documents .
clinical information was available for almost every patient whose data were accessed through the web hie interface ; the amount and type of data varied among patients based on the frequency and nature of their care in participating hospitals and clinics .
patients were offered the chance to  opt out from hie participation at the time of every encounter at participating hospitals and clinics .
the percentage of patients  opting out when consent was sought ranged from 1% to 3% across sites over the study period .
hie services were based on technologies created by vanderbilt university medical center and managed by a vanderbilt university team until a local governing board and a free - standing hie service provider assumed complete control in october 2010.27 data from contributing organizations were transmitted securely , tagged with meta - level descriptors , and stored in databases that allowed some organizational control and ensured access only when use complied with formal consent , data access , and data use policies.28 eleven of the 12 participating hospital eds accessed hie data only through a separate , secure web portal designed specifically for this purpose .
white board running list of patients registered in the ed over the previous 24  h , and displayed the number of encounter records available from other sites of care .
hie data were accessed in approximately 6.8% of ed encounters.24 ed physicians in the 12th hospital did not have access to the standard 
white board until the 10th month of the hie encounter data collection period . in earlier months
these summaries contained the dates of service , location of service , primary complaint , and international classification of disease , version 9 codes for every encounter in other hospitals .
physicians could obtain all of the information available to the other 11 sites through direct hie query .
when ed clinicians were given functionality identical to the direct access group , print encounter forms were abandoned and direct web access rates increased to regional norms.24 the study population was drawn from a tennessee hospital association hospital billing database consisting of all ed visit records from the 2-year study period ( january 2007 to december 2008 ) .
the initial hie exposure group case record set consisted of all 20  285 ed visits in which hie data access was documented through audit logs .
the hie access dates were from july 2007 to september 2008 ; 99.9% of hie encounter records were from the 13-month period from august 2007 to august 2008 .
a matching set of no - hie exposure group records was obtained by matching the case set with a corresponding number of ed visit records in which no hie data access was found .
each hie and no - hie encounter record was matched on age ( decile ) , gender , race , site of emergency care , presenting diagnosis , and primary payer source .
the no - hie encounter records were distributed across the entire 2-year study period to ensure adequate matching .
the 11 ed settings using the web - based system for the entire collection period were labeled 
direct access site 1 to  direct access site 11. the 12th ed first using print encounter forms and later switching to the same web - based system was labeled the 
study population characteristics variable included in regression models but not in case  control matching .
all research was conducted with institutional review board approval and under additional contractual restraints imposed by data - sharing agreements . from a set of 20  285 hie encounter records and a corresponding number of no - hie encounter records , 4487 hie encounter records and a corresponding number of
matched no - hie encounter records were excluded . of these excluded records , 932 were of patients who had at least one ed visit record in both the hie and no - hie encounter record sets ; another 3555 encounter records were for patients who had visited an ed during the study period but for whom their hie data were accessed only in non - ed care settings .
the final study population consisted of 15  798 hie encounter records and 15  798 matched no - hie encounter ( figure 1 ) .
 no - hie records were matched on age , gender , race , payer , presenting diagnosis , and site of care .
exclusions : case and control were same patients ( 932 ) ; case records were from non - ed settings ( 3555 ) .
seventeen records ( 0.1% ) had time stamp errors but were retained for regression analyses .
the number of ed encounter records in both the hie and no - hie sets were greater than the number of unique patients because some patients sought ed care more than once ( and often from more than one institution ) over the 2-year study period .
the 15  798 no - hie encounter records represented 13  832 unique patients . in the hie encounter record set ,
the number ( % ) of the 12  210 individuals seeking care one time , two times , or more than two times were 9728 ( 62% ) , 1704 ( 21% ) and 688 ( 17% ) , respectively .
corresponding values for the no - hie encounter records set were 12  479 ( 79% ) , 1014 ( 13% ) and 339 ( 8% ) , respectively .
of the patients in the hie encounter record set , 351 ( 2.8% ) had visit records from care both in the direct web access group and the mixed access site .
the number of corresponding patients in the no - hie encounter record set was 200 ( 1.4% ) .
the mixed access site differed from the 11-hospital direct access group both in the initial means of accessing hie data and in different hie use metrics resulting from employing printed encounter forms in the mixed access site .
for each , we independently examined rates of outcome variables in both the hie cohort and the no - hie cohort .
the outcome variables were : hospital admissions from the ed , head ct scans , body ct scans , chest radiographs , echocardiogram , outpatient surgery , the number of patients receiving one or more laboratory tests , admissions for observation , and ankle radiographs . in light of the computational requirements for this large dataset and the need to obtain estimates efficiently
, we matched each no - hie exposure with a corresponding hie exposure case on care delivery site , age group ( decile ) , gender , race , primary discharge diagnostic code , and health plan status .
our subsequent regression model controlled for admission type , charlson comorbidity index , age , health plan status , and length of stay ( table 1).29 to improve the calibration curve of our model , we applied a logarithmic transformation to the charlson comorbidity index and applied a 0.1 shift on to eliminate zero values .
some patients visited multiple eds ; the number of patients differed . to account for ed visit frequency differences
, we used generalized estimating equations ( gee ) employing a logit link function and a binomial distribution .
this method allowed us to compare the marginal probability of each outcome variable.30 
 31 our gee models adjusted for patient clustering initially through a working independent covariance matrix and subsequently by robust or sandwich se adjustment to quantify the uncertainty of our estimates .
we calculated the changes in each outcome variable by multiplying the differences in marginal probabilities of each outcome calculated from our fitted model by the total number of ed visits in which hie was actually accessed .
two - sided p values less than 0.05 were considered statistically significant . in order to estimate the potential financial savings , we chose a conservative approach in order to avoid over - generalizing the results or overestimating the financial savings .
in particular , the steps in our calculations were : let  denote the set of patients with hie exposure , and  denote all the procedures with significant hie effect;for i  , j  , we insert the controlled variables for the i patient into the j procedure gee model;assigning the hie variable a value of 1 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her care included access to hie data .
we denote the resulting probability as pi , j , hie;assigning the hie variable a value of 0 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her record care did not include access to hie data .
we denote the resulting probability as pi , j , non - hie;we calculate the mean probabilities of undergoing the j procedure for patients whose care included hie data access ( pj , hie ) , and for the same patients is if their care did not include hie data access ( pj , non - hie);potential cost savings were then defined as:(total potential cost saving of thejthprocedure=(cost of thejthprocedure)(only the number of encounters in which hie data were accessed)(pj , non - hiepj , hie ) .
let  denote the set of patients with hie exposure , and  denote all the procedures with significant hie effect ; for i  , j  , we insert the controlled variables for the i patient into the j procedure gee model ; assigning the hie variable a value of 1 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her care included access to hie data .
we denote the resulting probability as pi , j , hie ; assigning the hie variable a value of 0 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her record care did not include access to hie data .
we denote the resulting probability as pi , j , non - hie ; we calculate the mean probabilities of undergoing the j procedure for patients whose care included hie data access ( pj , hie ) , and for the same patients is if their care did not include hie data access ( pj , non - hie ) ; potential cost savings were then defined as:(total potential cost saving of thejthprocedure=(cost of thejthprocedure)(only the number of encounters in which hie data were accessed)(pj , non - hiepj , hie ) .
costs were defined as median regional costs during the study period obtained from the tennessee hospital association .
for both the 11-ed direct web access group and the single mixed access site we separately calculated the societal financial impact by summing the cost differences associated with each outcome variable in which statistically significant differences were observed.32 we calculated annual savings from our 13-month results by multiplying the estimated total savings by ( 12/13 ) .
our financial impact results are based only on the study ed encounters in which hie was accessed .
since 2005 , the non - profit midsouth ehealth alliance has governed and managed hie services among 16 major healthcare provider organizations in the memphis , tennessee , usa , metropolitan area .
all 12 major hospitals provided hospital discharge summary notes , laboratory data , pathology reports , radiographic reports , other transcribed notes , and a range of other clinical and administrative documents .
all participating clinics provided demographic information , registration information , and a limited number of clinical data types .
clinicians began accessing health information exchange data in their eds in may 2006 and later obtained access on hospital wards and in ambulatory clinics .
hie access in eds was phased in over the study period ; all major hospital ed had access to hie before collecting hie use data .
as of 1 october 2010 , clinicians had access to over 7.5 million encounter records on 1.7 million patients , 4.9 million chief complaints , 45 million laboratory tests , 5 million radiology reports , and 2.1 million other reports and documents .
clinical information was available for almost every patient whose data were accessed through the web hie interface ; the amount and type of data varied among patients based on the frequency and nature of their care in participating hospitals and clinics .
patients were offered the chance to  opt out from hie participation at the time of every encounter at participating hospitals and clinics .
the percentage of patients  opting out when consent was sought ranged from 1% to 3% across sites over the study period .
hie services were based on technologies created by vanderbilt university medical center and managed by a vanderbilt university team until a local governing board and a free - standing hie service provider assumed complete control in october 2010.27 data from contributing organizations were transmitted securely , tagged with meta - level descriptors , and stored in databases that allowed some organizational control and ensured access only when use complied with formal consent , data access , and data use policies.28 eleven of the 12 participating hospital eds accessed hie data only through a separate , secure web portal designed specifically for this purpose .
white board running list of patients registered in the ed over the previous 24  h , and displayed the number of encounter records available from other sites of care .
hie data were accessed in approximately 6.8% of ed encounters.24 ed physicians in the 12th hospital did not have access to the standard 
white board until the 10th month of the hie encounter data collection period . in earlier months
these summaries contained the dates of service , location of service , primary complaint , and international classification of disease , version 9 codes for every encounter in other hospitals .
physicians could obtain all of the information available to the other 11 sites through direct hie query .
when ed clinicians were given functionality identical to the direct access group , print encounter forms were abandoned and direct web access rates increased to regional norms.24
the study population was drawn from a tennessee hospital association hospital billing database consisting of all ed visit records from the 2-year study period ( january 2007 to december 2008 ) .
the initial hie exposure group case record set consisted of all 20  285 ed visits in which hie data access was documented through audit logs .
the hie access dates were from july 2007 to september 2008 ; 99.9% of hie encounter records were from the 13-month period from august 2007 to august 2008 .
a matching set of no - hie exposure group records was obtained by matching the case set with a corresponding number of ed visit records in which no hie data access was found .
each hie and no - hie encounter record was matched on age ( decile ) , gender , race , site of emergency care , presenting diagnosis , and primary payer source .
the no - hie encounter records were distributed across the entire 2-year study period to ensure adequate matching .
the 11 ed settings using the web - based system for the entire collection period were labeled 
direct access site 1 to  direct access site 11. the 12th ed first using print encounter forms and later switching to the same web - based system was labeled the 
study population characteristics variable included in regression models but not in case  control matching .
all research was conducted with institutional review board approval and under additional contractual restraints imposed by data - sharing agreements . from a set of 20  285 hie encounter records and a corresponding number of no - hie encounter records , 4487 hie encounter records and a corresponding number of matched no - hie encounter records
932 were of patients who had at least one ed visit record in both the hie and no - hie encounter record sets ; another 3555 encounter records were for patients who had visited an ed during the study period but for whom their hie data were accessed only in non - ed care settings .
the final study population consisted of 15  798 hie encounter records and 15  798 matched no - hie encounter ( figure 1 ) .
 no - hie records were matched on age , gender , race , payer , presenting diagnosis , and site of care .
exclusions : case and control were same patients ( 932 ) ; case records were from non - ed settings ( 3555 ) .
seventeen records ( 0.1% ) had time stamp errors but were retained for regression analyses .
the number of ed encounter records in both the hie and no - hie sets were greater than the number of unique patients because some patients sought ed care more than once ( and often from more than one institution ) over the 2-year study period .
the 15  798 no - hie encounter records represented 13  832 unique patients . in the hie encounter record set ,
the number ( % ) of the 12  210 individuals seeking care one time , two times , or more than two times were 9728 ( 62% ) , 1704 ( 21% ) and 688 ( 17% ) , respectively .
corresponding values for the no - hie encounter records set were 12  479 ( 79% ) , 1014 ( 13% ) and 339 ( 8% ) , respectively .
of the patients in the hie encounter record set , 351 ( 2.8% ) had visit records from care both in the direct web access group and the mixed access site .
the number of corresponding patients in the no - hie encounter record set was 200 ( 1.4% ) .
the mixed access site differed from the 11-hospital direct access group both in the initial means of accessing hie data and in different hie use metrics resulting from employing printed encounter forms in the mixed access site .
for each , we independently examined rates of outcome variables in both the hie cohort and the no - hie cohort .
the outcome variables were : hospital admissions from the ed , head ct scans , body ct scans , chest radiographs , echocardiogram , outpatient surgery , the number of patients receiving one or more laboratory tests , admissions for observation , and ankle radiographs . in light of the computational requirements for this large dataset and the need to obtain estimates efficiently
, we matched each no - hie exposure with a corresponding hie exposure case on care delivery site , age group ( decile ) , gender , race , primary discharge diagnostic code , and health plan status .
our subsequent regression model controlled for admission type , charlson comorbidity index , age , health plan status , and length of stay ( table 1).29 to improve the calibration curve of our model , we applied a logarithmic transformation to the charlson comorbidity index and applied a 0.1 shift on to eliminate zero values .
some patients visited multiple eds ; the number of patients differed . to account for ed visit frequency differences
, we used generalized estimating equations ( gee ) employing a logit link function and a binomial distribution .
this method allowed us to compare the marginal probability of each outcome variable.30 
 31 our gee models adjusted for patient clustering initially through a working independent covariance matrix and subsequently by robust or sandwich se adjustment to quantify the uncertainty of our estimates .
we calculated the changes in each outcome variable by multiplying the differences in marginal probabilities of each outcome calculated from our fitted model by the total number of ed visits in which hie was actually accessed .
in order to estimate the potential financial savings , we chose a conservative approach in order to avoid over - generalizing the results or overestimating the financial savings .
in particular , the steps in our calculations were : let  denote the set of patients with hie exposure , and  denote all the procedures with significant hie effect;for i  , j  , we insert the controlled variables for the i patient into the j procedure gee model;assigning the hie variable a value of 1 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her care included access to hie data .
we denote the resulting probability as pi , j , hie;assigning the hie variable a value of 0 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her record care did not include access to hie data .
we denote the resulting probability as pi , j , non - hie;we calculate the mean probabilities of undergoing the j procedure for patients whose care included hie data access ( pj , hie ) , and for the same patients is if their care did not include hie data access ( pj , non - hie);potential cost savings were then defined as:(total potential cost saving of thejthprocedure=(cost of thejthprocedure)(only the number of encounters in which hie data were accessed)(pj , non - hiepj , hie ) .
let  denote the set of patients with hie exposure , and  denote all the procedures with significant hie effect ; for i  , j  , we insert the controlled variables for the i patient into the j procedure gee model ; assigning the hie variable a value of 1 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her care included access to hie data .
we denote the resulting probability as pi , j , hie ; assigning the hie variable a value of 0 in the model , we obtain the probability of ordering the j procedure for the i patient if his / her record care did not include access to hie data .
we denote the resulting probability as pi , j , non - hie ; we calculate the mean probabilities of undergoing the j procedure for patients whose care included hie data access ( pj , hie ) , and for the same patients is if their care did not include hie data access ( pj , non - hie ) ; potential cost savings were then defined as:(total potential cost saving of thejthprocedure=(cost of thejthprocedure)(only the number of encounters in which hie data were accessed)(pj , non - hiepj , hie ) .
costs were defined as median regional costs during the study period obtained from the tennessee hospital association .
for both the 11-ed direct web access group and the single mixed access site we separately calculated the societal financial impact by summing the cost differences associated with each outcome variable in which statistically significant differences were observed.32 we calculated annual savings from our 13-month results by multiplying the estimated total savings by ( 12/13 ) .
our financial impact results are based only on the study ed encounters in which hie was accessed .
after controlling for all covariates through our regression model , hie use in the direct web access group resulted in 191 fewer admissions than would have been predicted to occur without hie use ( or 0.271 ; 95% ci 0.210 to 0.351 ; p<0.001 ) .
similarly , hie access resulted in 800 additional chest radiographs ( or 4.367 ; 95% ci 3.784 to 5.040 ; p<0.001 ) and 74 additional head ct studies ( or 5.032 ; 95% ci 3.074 to 8.236 ) .
the total financial savings were us$862  425 ( 95% ci us$671  884 to us$1  067  026 ) . the calculated annual financial savings from the 11-hospital direct access group were approximately us$796  085 ( table 2 ) .
direct web access group : differences in frequency of occurrence of characteristics ( n=5019 ) * logistic regression analysis using generalized estimating equations ( logit link function and binomial distribution ) ; controlled for admission type , age , health plan status , logarithmic transformation of the charlson comorbidity index and length of stay .
marginal pnon - hie = the mean probability of these procedures if patients with hie exposure were not accessed by hie .
difference = ( number of encounters from hie exposure patients )  ( marginal phiemarginal pnon - hie ) .
after controlling for all covariates through our regression model , hie use at the mixed access site resulted in 221 fewer admissions than would have been predicted to occur without hie use ( or 0.478 ; 95% ci 0.402 to 0.568 ; p<0.001 ) .
similarly , hie access resulted in 103 fewer head ct ( or 0.913 ; 95% ci 0.842 to 0.991 ; p=0.029 ) , 196 fewer body ct ( or 0.886 ; 95% ci 0.828 to 0.948 ; p<0.001 ) , and 258 fewer instances in which laboratory tests were ordered ( or 0.880 ; 95% ci 0.828 to 0.935 ; p<0.001 ) .
the total financial savings were us$1  247  331 ( 95% ci us$991  927 to us$1  498  776 ) . the calculated annual financial savings from the mixed access site were approximately us$1  151  382 ( table 3 ) .
mixed access site : differences in frequency of occurrence of characteristics ( n=10  779 ) * logistic regression analysis using generalized estimating equations ( logit link function and binomial distribution ) ; controlled for admission type , age , health plan status , logarithmic transformation of charlson comorbidity index and length of stay .
marginal pnon - hie = the mean probability of these procedures patients with hie exposure were not accessed by hie .
difference = ( number of encounters from hie exposure patients )  ( marginal phie  marginal pnon - hie ) .
the net societal savings were therefore approximately us$1.07 million . reduced admissions from eds account for 97.6% of the total savings ( table 4 ) .
study population societal savings over the 2-year study period the month preceding and the month following the 13-month period contained 17 records ( 0.11% ) of the total set .
after controlling for all covariates through our regression model , hie use in the direct web access group resulted in 191 fewer admissions than would have been predicted to occur without hie use ( or 0.271 ; 95% ci 0.210 to 0.351 ; p<0.001 ) .
similarly , hie access resulted in 800 additional chest radiographs ( or 4.367 ; 95% ci 3.784 to 5.040 ; p<0.001 ) and 74 additional head ct studies ( or 5.032 ; 95% ci 3.074 to 8.236 ) .
the total financial savings were us$862  425 ( 95% ci us$671  884 to us$1  067  026 ) . the calculated annual financial savings from the 11-hospital direct access group were approximately us$796  085 ( table 2 ) .
direct web access group : differences in frequency of occurrence of characteristics ( n=5019 ) * logistic regression analysis using generalized estimating equations ( logit link function and binomial distribution ) ; controlled for admission type , age , health plan status , logarithmic transformation of the charlson comorbidity index and length of stay .
marginal pnon - hie = the mean probability of these procedures if patients with hie exposure were not accessed by hie .
difference = ( number of encounters from hie exposure patients )  ( marginal phiemarginal pnon - hie ) .
after controlling for all covariates through our regression model , hie use at the mixed access site resulted in 221 fewer admissions than would have been predicted to occur without hie use ( or 0.478 ; 95% ci 0.402 to 0.568 ; p<0.001 ) . similarly , hie access resulted in 103 fewer head ct ( or 0.913 ; 95% ci 0.842 to 0.991 ; p=0.029 ) , 196 fewer body ct ( or 0.886 ; 95% ci 0.828 to 0.948 ; p<0.001 ) , and 258 fewer instances in which laboratory tests were ordered ( or 0.880 ; 95% ci 0.828 to 0.935 ; p<0.001 ) .
the total financial savings were us$1  247  331 ( 95% ci us$991  927 to us$1  498  776 ) . the calculated annual financial savings from the mixed access site were approximately us$1  151  382 ( table 3 ) .
mixed access site : differences in frequency of occurrence of characteristics ( n=10  779 ) * logistic regression analysis using generalized estimating equations ( logit link function and binomial distribution ) ; controlled for admission type , age , health plan status , logarithmic transformation of charlson comorbidity index and length of stay .
marginal pnon - hie = the mean probability of these procedures patients with hie exposure were not accessed by hie .
difference = ( number of encounters from hie exposure patients )  ( marginal phie  marginal pnon - hie ) .
annual operating costs during the study period were approximately us$880  000 . the net societal savings were therefore approximately us$1.07 million . reduced admissions from eds account for 97.6% of the total savings ( table 4 ) .
study population societal savings over the 2-year study period the month preceding and the month following the 13-month period contained 17 records ( 0.11% ) of the total set .
we present evidence that relatively limited use measured only in ed settings can confer net societal financial benefit across an entire region .
we found approximately us$800 in annual savings within 11 hospital ed accessing hie data through a simple web - based technology , and a total of us$1.9 million in annual savings if all regional hospital eds accessing hie through different means are included . because hie operational costs were kept very low ( us$880  000 per year ) , net savings are over us$1 million .
the memphis ed we studied accounted for virtually all emergency care for the 1.2 million individuals in the metropolitan area . assuming the per capita healthcare expenditures are similar to national estimates of us$8026 , the investment to support hie is only slightly more than one one - hundredth of the total per capita healthcare expenditures . seeking to measure impact across the entire region
, we sought to identify a method of analysis that was both extremely conservative and at the same time accounted for the wide differences in patient populations , practice patterns , and uses of technology .
accordingly , we rigorously associated every hie access encounter record with a matched no - hie access record .
we incorporated these records into regression analysis that also accounted for disease severity and other measurable factors .
this model allowed us to estimate actual increases or decreases in specific activities based on whether or not hie had been used .
we used these differences to calculate the adjusted financial impact of hie only on the patients for whom hie data were accessed in the course of their care .
access to hie data was relatively low ( 6.8% ) because the data were not integrated into and presented through the many different electronic health record ( ehr ) systems used within the region .
previous work in this population suggests there are differences between the study patients for whom hie data were accessed and those for whom it was not.24 providers use hie when they encounter clinical problems in which additional information may provide insight . the variation in intent among ed clinicians
we speculate that the small but significant increase in chest x - ray use in inner - city ed within the direct access group is an example of differences in provider motivation .
our observations support the findings of others who suggest that providers find hie helpful in managing non - urgent chronic medical conditions  particularly for indigent populations seeking care in more than one setting.22 as is the case across the country , many ed visits were for chronic or non - urgent medical conditions33 
 34 our hie system was used more frequently for  repeat visits.24 our approach did not allow us to account for provider clustering effects .
such clustering effects would affect standard errors and ci but not the estimated increases or reductions in outcome variables .
our findings suggest that widespread hospital support for hie and initial deployment only in ed settings will still lower overall societal costs . in our case , these societal cost reductions were of sufficient magnitude to offset the entire cost of operations .
initial use of hie data in urban ed settings confers immediate trust and value among key hospital stakeholders , but far greater financial and clinical benefit is anticipated when hie is ubiquitous across a far wider array of care settings . if care delivery within a region is highly fragmented , collaborative hie among an entire community may confer far greater benefit by improving care coordination , measuring and addressing hospital readmissions , and collectively developing more comprehensive quality metrics for both individuals and populations . as has been our own experience , we assume that basic hie services can be provided
inexpensively and that hie once established can expand at relatively low cost to encompass a broader array of clinicians employing certified ehr .
federal efforts in ehr certification , national data standards , and meaningful use incentives will make the integration of medical practices into hie networks far less costly than was the case when our effort began in 2004 .
our study demonstrates a positive financial impact on communities , but we believe these savings will be only a fraction of the economic benefit that will be realized as our connected digital healthcare delivery system evolves .
 objectiveto examine the financial impact health information exchange ( hie ) in emergency departments ( eds).materials and methodswe studied all ed encounters over a 13-month period in which hie data were accessed in all major emergency departments memphis , tennessee . 
 hie access encounter records were matched with similar encounter records without hie access . 
 outcomes studied were ed - originated hospital admissions , admissions for observation , laboratory testing , head ct , body ct , ankle radiographs , chest radiographs , and echocardiograms . 
 our estimates employed generalized estimating equations for logistic regression models adjusted for admission type , length of stay , and charlson co - morbidity index . 
 marginal probabilities were used to calculate changes in outcome variables and their financial consequences.resultshie data were accessed in approximately 6.8% of ed visits across 12 eds studied . in 11 eds directly accessing hie data only through a secure web browser , access was associated with a decrease in hospital admissions ( adjusted odds ratio ( or)=0.27 ; p<0001 ) . in a 12th ed relying more on print summaries , 
 hie access was associated with a decrease in hospital admissions ( or=0.48 ; p<0001 ) and statistically significant decreases in head ct use , body ct use , and laboratory test ordering.discussionapplied only to the study population , hie access was associated with an annual cost savings of $ 1.9 million . 
 net of annual operating costs , hie access reduced overall costs by $ 1.07 million . 
 hospital admission reductions accounted for 97.6% of total cost reductions.conclusionaccess to additional clinical data through hie in emergency department settings is associated with net societal saving . 
Methods
Setting
Study population
Analysis
Financial impact analysis
Results
The direct web access group
The mixed access site
Overall financial impact
Discussion